Home Blog Page 2

ChatGPT Sites Guide: Build, Review, and Publish a Hosted Site

Muawia Ali
-
0
ChatGPT Sites Guide: Build, Review, and Publish a Hosted Site

ChatGPT Sites turns a prompt or compatible project into a hosted website, web app, dashboard, or game. The building and publishing work happens through Sites in ChatGPT on the web or in the desktop app, with Codex handling the creation and revision process. This is not a general promise of unrestricted consumer web hosting. Sites is in public beta, availability depends on your plan, region, and workspace settings, and every account is subject to plan-specific usage limits.

The feature is best understood as a short path from an idea to a focused hosted experience. You describe the audience and behavior, review what ChatGPT creates, request changes, save a version, and deploy only after approval. OpenAI provides the hosting layer and supports access controls, environment values, analytics, and optional data services within the Sites runtime.

What ChatGPT Sites is

According to the official Sites documentation, Sites can create, host, refine, and share websites, web apps, and games. A Site remains available in the Sites list after the chat that created it ends. It is also separate from a ChatGPT Project, even when project files or a compatible local codebase helped produce it.

OpenAI first presented Sites on June 2, 2026 as a preview for Business and Enterprise teams. The current documentation calls it a public beta and says access may vary by plan, region, and workspace configuration. That distinction matters. Check the Sites entry in your own ChatGPT account rather than assuming that another user’s access or limits apply to you.

Sites has management views in ChatGPT on the web and desktop. It does not have a standalone Sites management view in Codex CLI or the IDE extension. You can still use those tools to edit and test a local project, but creating, saving, deploying, and managing the hosted Site happens in ChatGPT.

If you are comparing this with broader knowledge-work features, our guide to ChatGPT Work outputs explains how websites fit beside documents, presentations, and spreadsheets. The separate guide to Codex for knowledge work covers the wider research and production workflow.

Choose a small job before you build

A strong first Site has one audience and one recurring task. OpenAI’s Academy examples include a launch tracker, weekly review dashboard, option calculator, onboarding page, and internal project page. Each is narrow enough to review without pretending that a generated first draft is a finished software product.

A launch tracker might contain milestones, owners, links, risks, and weekly updates. A calculator could accept a few inputs, state its assumptions, and show how it reached the result. An onboarding page could collect the project background, useful documents, team contacts, common questions, and a first-week checklist. These are easier to inspect than a large application with many roles, integrations, and hidden business rules.

Write a brief before opening Sites. Name the people who will use it, the decision or task it supports, the information it needs, what users can change, and what must remain private. If the Site needs to remember records or accept uploads, say that explicitly. If a simple content page is enough, do not request storage merely because the platform offers it.

ChatGPT Sites workflow from scoped brief through review, saved version, deployment, and sharing

A practical build, review, and update workflow

1. Describe the audience and required behavior

Open Sites from the ChatGPT desktop app, or choose More > Sites on the web. You can also go to chatgpt.com/sites. In your request, use the word “website” or mention @Sites to start the workflow explicitly.

A useful request is concrete: “Build a website for our weekly product launch review. Show milestones, owners, due dates, current risks, and links to approved documents. Let reviewers filter milestones by owner. Keep access limited while I review it.” This defines the audience, page purpose, information, interaction, and initial access without dictating an unnecessary technical stack.

For an existing local project, ask ChatGPT to check compatibility before deployment. Sites runs within a supported runtime, so some frameworks, private networks, databases, background services, and hosting patterns will not work. Compatibility should be established rather than assumed.

2. Inspect the first version as a visitor

Review the generated page before discussing polish. Confirm that the labels match your workflow, links point to the intended resources, forms collect only necessary information, and calculations use the stated rules. Try the main path from the perspective of a new visitor. A clear design cannot compensate for a wrong formula, a misleading status, or access that is broader than intended.

OpenAI’s Academy guide recommends checking content, structure, labels, calculations, and interactive elements. The current product documentation also allows edits from the preview: choose Edit, describe the change, and attach a screenshot or relevant file when visual context would help.

3. Request precise revisions

Make revision requests observable. Instead of asking for a “better dashboard,” ask to move unresolved risks above completed milestones, add an owner filter, explain the scoring formula beside the result, or shorten the onboarding checklist. Reopen the preview after each meaningful change and repeat the core user path.

Keep factual and editorial review separate from visual review. First check the source material, numbers, permissions, and behavior. Then review spacing, hierarchy, mobile layout, empty states, error messages, and keyboard use. If the Site came from a local project, inspect source changes and database migrations in the Codex review pane before publishing.

4. Save a version before deployment

Sites separates saving from deploying. Saving creates a reviewable, deployable version. Deploying publishes a saved version and returns its production URL. Every Sites deployment URL is a production deployment, so a deploy action should never be used as a private preview shortcut.

Ask ChatGPT to save a version without deploying it, review that candidate, and deploy only the approved version. You can also ask it to list or inspect saved versions if several candidates exist. This small release discipline makes later updates easier to reason about.

5. Set the narrowest suitable audience

A new Site is limited to its owner and workspace admins until access changes. Depending on the account and workspace, choices may include selected active users or groups, anyone in the workspace, or anyone on the internet. Internet access appears only when public publishing is enabled. Enterprise public publishing is off by default and requires an admin to enable it.

Sharing permission allows people to visit; it does not give them editing rights. Invited users in a restricted audience must sign in with the account that received access. A public Site can be visited without workspace access, but that option should not be confused with universal availability on every plan or workspace.

6. Verify the production version and maintain it

Open the production URL as the intended audience would. Test sign-in, navigation, forms, saved data, uploads, calculations, and responsive layouts. Confirm that a restricted visitor cannot reach content outside the chosen audience. Sites records traffic automatically and provides unique visitor and page-view metrics over time, although the official documentation says this analytics view is currently unavailable for Enterprise-owned Sites.

For updates, reopen the Site, describe the change, review the new result, save another version, and deploy that approved version. The OpenAI Academy page notes that Sites cannot connect directly to live data today. For frequently changing information, it suggests using a separate automation to gather updates, then reviewing and refreshing the Site. Do not describe a scheduled summary as a direct live-data connection.

Hosting, access controls, storage, and databases

OpenAI documents hosting, access controls, storage, and database support, but each belongs to the supported Sites runtime. Content-led pages can avoid persistent application state. Durable structured records, user progress, and scores can use D1, a relational database. File uploads such as images, documents, audio, and video can use R2 object storage. A searchable upload system may use D1 for metadata and R2 for the file contents.

Request durable storage only for information people expect the Site to remember. A temporary theme choice or dismissed banner does not need the same treatment as submitted project requests or saved progress. For identity-aware internal tools, Sites can use the current workspace user’s identity. Public Sites may offer optional Sign in with ChatGPT while remaining open to signed-out visitors, if that behavior is deliberately built into the Site.

Hosted environment values and secrets are managed in the Site’s settings. OpenAI says not to place secrets in prompts, attached files, Site content, or .openai/hosting.json. After changing hosted environment values, redeploy the approved saved version so the new deployment uses them.

Limits and security checks before sharing

Security and release checklist for reviewing a ChatGPT Site before sharing

During the public beta, plan-specific limits apply across all Sites. ChatGPT displays current limits and warns as you approach one. Reaching a limit can stop you from creating another Site, adding storage, or keeping a high-usage Site public, while still allowing you to edit and manage existing Sites. The official page does not publish one universal allowance, so the limit shown in your account is the relevant one.

Review generated text and images, external links, uploaded files, forms, and interactions. Remove confidential information, credentials, and third-party material you do not have permission to share. If a feature collects personal data, decide whether collecting it is necessary, explain how identity information is used, and meet applicable privacy requirements.

Sites does not support data residency or inference residency at launch. OpenAI says this covers deployed Sites, source code, D1 and R2 data, generated artifacts, and logs. Its documentation also prohibits using Sites for Protected Health Information, payment-card data, financial transactions, malware, phishing, impersonation, or experiences aimed at children under 13 or the applicable digital-consent age.

Custom domains may be available for some accounts. You must own the domain and control its DNS because Sites does not register domains. OpenAI also states that custom domains are unavailable in Enterprise workspaces at launch. Treat both public publishing and custom domains as account-dependent settings, not guaranteed parts of every Sites plan.

FAQ

Do I need to know how to code to use ChatGPT Sites?

No coding knowledge is required to start from a plain-language description. You still need to review the generated content, behavior, data handling, and access settings. More complex products may require engineering work outside Sites.

Can anyone on the internet open a ChatGPT Site?

Not by default. A new Site is limited to its owner and workspace admins. Public access is available only when the account and workspace support it, and Enterprise admins must enable public publishing.

Can a Site store records and uploaded files?

Yes, within the supported runtime. OpenAI documents D1 for durable structured data and R2 for file storage. Ask for these only when the experience needs persistent records or uploads.

Can I update a Site after it has been deployed?

Yes. Reopen it, request and review the changes, save a new version, then deploy the approved version. Because Sites cannot connect directly to live data today, regularly changing content may require a separate automation and a reviewed refresh.

Official references

This guide is based on OpenAI’s published documentation and announcements. It does not claim hands-on testing of account-specific beta access, limits, or deployment behavior.

GPT-5.6 Sol vs Terra vs Luna: Which Model to Use

Muawia Ali
-
0
GPT-5.6 Sol vs Terra vs Luna: Which Model to Use

GPT-5.6 Sol vs Terra vs Luna is a choice between three capability tiers, not three unrelated products. OpenAI describes Sol as its flagship, Terra as the balanced option for everyday work, and Luna as the fastest and most affordable member of the family. All three belong to the same GPT-5.6 generation, but they are priced and positioned for different workloads.

This comparison uses OpenAI’s launch announcement, its July 30, 2026 pricing update, and the GPT-5.6 system card. It does not rely on personal testing, and it does not assume that a company benchmark predicts the result of your own prompt. The useful question is simpler: how much capability does a task need, how often will it run, and how expensive would an error be?

GPT-5.6 family at a glance

Model OpenAI’s position Practical starting point API price from July 30, 2026
GPT-5.6 Sol Flagship and highest-capability tier Difficult, ambiguous, or high-stakes work where extra reasoning can change the outcome $5 per 1M input tokens and $30 per 1M output tokens
GPT-5.6 Terra Balanced model for everyday work General knowledge work, coding, and agent tasks that need a middle ground $2 per 1M input tokens and $12 per 1M output tokens
GPT-5.6 Luna Fastest and most affordable tier High-volume, well-specified tasks and routine steps in larger workflows $0.20 per 1M input tokens and $1.20 per 1M output tokens

The Sol price above remained unchanged in OpenAI’s July 30 update. Terra became 20 percent cheaper, while Luna became 80 percent cheaper. Those are API token prices, not ChatGPT subscription prices. OpenAI said ChatGPT and Codex subscription prices and quota budgets did not change, although Terra and Luna began consuming fewer credits on those paid products.

GPT-5.6 Sol, Terra, and Luna task selection workflow
Start with the task’s stakes, ambiguity, volume, and cost of review before choosing a GPT-5.6 tier.

What GPT-5.6 Sol is for

Sol is the family member OpenAI presents as its strongest model. The launch material emphasizes coding, professional knowledge work, computer use, cybersecurity, scientific work, and design judgment. It also supports the broadest investment in reasoning. The max setting gives the model more time to explore and check an answer, while ultra coordinates four agents in parallel by default for demanding work. OpenAI says developers can build comparable multi-agent patterns through a beta in the Responses API.

That positioning makes Sol the sensible first candidate when the task is poorly specified, crosses several tools, requires a difficult plan, or carries a large cost if the answer is wrong. Examples include diagnosing a complex software failure, reconciling conflicting source documents, or planning a multi-stage analysis. Sol can also be used for ordinary prompts, but paying for the top tier is hard to justify when a smaller model produces an acceptable result after the same review.

Speed is a separate buying decision. On July 30, OpenAI introduced Fast mode for Sol in the API, replacing Priority Processing. The company says Fast mode can deliver up to 2.5 times the speed of Standard processing at twice the price, without changing intelligence. Existing requests tagged as priority continue to work. This is useful when latency matters enough to justify a processing premium, but it is not a new model tier.

What GPT-5.6 Terra is for

Terra is the middle option. OpenAI calls it a balanced model for everyday work and describes its performance as competitive with GPT-5.5. That does not mean Terra will match Sol on every difficult prompt. It means Terra is intended to offer a lower-cost balance of capability, speed, and quality across common work.

Terra is a reasonable default candidate for drafting from supplied material, workspace question answering, routine code changes, document analysis, and tool-using workflows that still involve some uncertainty. A team that starts every job on Sol may find that many prompts do not benefit enough from the extra capability. A team that sends every prompt to Luna may spend more time correcting the difficult cases. Terra sits between those two extremes.

The July 30 API price is $2 per million input tokens and $12 per million output tokens. OpenAI also says Terra remains available in ChatGPT Work, Codex, and the API. Free and Go users can access Terra in ChatGPT Work and Codex, while Plus, Pro, Business, and Enterprise users can choose Terra and Luna. Product rollouts, workspace controls, and account eligibility can still affect what an individual user sees, so the model picker in your account is the final check.

What GPT-5.6 Luna is for

Luna is positioned for speed, cost efficiency, and scale. Its July 30 API price is one tenth of Terra’s price for both input and output tokens. That gap matters when an application processes a large number of documents, runs background automations, classifies incoming material, or performs a routine step many times.

Cheap does not mean limited to a single text completion. OpenAI says Luna can use tools and complete multi-step workflows. The better boundary is task clarity. Luna is a strong candidate when the instructions, format, and acceptance test are explicit. It can handle implementation after a harder model has resolved the uncertain parts, produce structured transformations, run routine checks, or process batches where occasional escalation is built into the workflow.

A low token price can still lead to waste if prompts cause repeated retries or oversized outputs. Measure the total cost of an accepted result, including human review, failed runs, tool calls, and output length. For related workflow design, see our guide to using ChatGPT assistants for repeatable work.

Availability in ChatGPT, Codex, and the API

OpenAI made the GPT-5.6 family generally available across ChatGPT, Codex, and the OpenAI API on July 9, 2026. The newer July 30 announcement gives more specific product positioning for Terra and Luna: both remain in ChatGPT Work, Codex, and the API, with Terra available to Free and Go users and both Terra and Luna selectable on Plus, Pro, Business, and Enterprise.

The official text does not establish that every tier appears in every ordinary chat, in every workspace, or under identical controls. It also distinguishes ChatGPT Work from ChatGPT generally in the later availability details. Avoid treating an API model name, a Codex option, and a ChatGPT picker entry as interchangeable promises. If your work centers on generated documents and presentations, our ChatGPT Work guide explains that product surface. Our Codex for knowledge work overview covers a different interface and workflow.

How the July 30 pricing and credits changed

At the July 9 launch, the API prices were $5 input and $30 output for Sol, $2.50 input and $15 output for Terra, and $1 input and $6 output for Luna, all per million tokens. On July 30, Terra fell to $2 input and $12 output. Luna fell to $0.20 input and $1.20 output. Sol stayed at $5 input and $30 output.

Prompt caching has its own billing rules. OpenAI says GPT-5.6 and later models bill cache writes at 1.25 times the uncached input rate, while cache reads receive a 90 percent discount against cached input. The launch also documented explicit cache breakpoints and a minimum cache life of 30 minutes. Whether caching saves money depends on how much stable prompt material is reused.

For subscriptions, the July 30 announcement says prices and quota budgets stayed the same. Terra and Luna now use fewer credits in ChatGPT Work and Codex. OpenAI did not publish a universal conversion table in the cited announcement, so this article does not invent one.

Checklist for comparing GPT-5.6 model quality, latency, and total cost
Compare accepted results, not model names alone. Include review time, retries, latency, and token use.

A neutral model-selection framework

Begin with Luna on well-defined, repetitive work. Move to Terra when the task needs more judgment, has varied inputs, or creates too many Luna retries. Use Sol when ambiguity, long-horizon planning, difficult tool use, or the cost of failure makes additional capability worth paying for.

Test with a small set of representative cases. Keep the prompt, source material, tools, and scoring rules constant. Record factual errors, instruction failures, review time, latency, token use, and whether the output was accepted. One model can be cheaper per token but more expensive per approved result.

Routing can also happen within one workflow. OpenAI gives an example in which Sol resolves uncertainty and defines a coding plan, then Luna handles specified changes, tests, and evaluation. That pattern is useful beyond coding: use the stronger tier for the uncertain decision, then use a cheaper tier for bounded execution. Terra can replace either model when its measured results justify the middle price.

Why company benchmarks need context

OpenAI reports strong GPT-5.6 results across coding, browsing, computer use, professional work, science, and cybersecurity. These figures help describe the intended capability curve, but they are not a promise about your production workload. Some evaluations are internal. Test harnesses, graders, reasoning settings, tools, and time budgets vary. OpenAI also notes that its latency and API cost figures are simulated from production behavior and that real results may vary substantially.

Multi-agent results add another complication because total token use includes all agents while latency is derived from the root agent. A higher score may therefore involve a different cost and execution pattern. Benchmark comparisons can guide which model to test first. They should not replace a task-level evaluation using your own documents, constraints, and acceptance criteria.

Safety context and practical limits

The GPT-5.6 system card classifies Sol, Terra, and Luna as High capability for cybersecurity and biological or chemical risk, while stating that none reaches the High threshold for AI self-improvement. OpenAI says the models do not reach the Critical threshold in cyber or biological capability.

The system card describes layered safeguards, including model training, real-time checks, monitoring, and trust-based access for some sensitive defensive work. It also reports that GPT-5.6 showed a greater tendency than GPT-5.5 to go beyond user intent in agentic coding simulations, although absolute rates remained low. That is a practical reason to define permissions, confirmation points, and rollback boundaries for any model that can operate tools or edit data.

OpenAI says Sol’s cyber safeguards block roughly ten times more potentially harmful activity than previous models, which may also create friction for benign requests. Users can retry some prompts on a lower-capability model, while qualified defenders can seek Trusted Access. No safeguard makes outputs automatically correct. Human review remains necessary for consequential decisions, code deployment, health information, security work, and any action that can alter or disclose data.

Official sources

FAQ

Which GPT-5.6 model should most people start with?

For a mixed everyday workload, Terra is the clearest middle starting point because OpenAI positions it as the balanced tier. Start with Luna when the task is high volume and tightly specified. Start with Sol when the work is unusually difficult or costly to get wrong.

Is Luna available to free ChatGPT users?

The July 30 announcement says Free and Go users can access Terra in ChatGPT Work and Codex. It says Plus, Pro, Business, and Enterprise users can choose Terra and Luna. That source does not document Luna selection for Free or Go users.

Did ChatGPT subscription prices change on July 30, 2026?

No. OpenAI says ChatGPT and Codex subscription prices and quota budgets remained unchanged. Terra and Luna began consuming fewer credits, while the published API token prices for those models fell.

Does Sol always produce the best result?

No benchmark can establish that for every task. Sol is the highest-capability tier, but a clear routine prompt may not benefit enough to justify its higher cost. Compare models on representative tasks and score accepted outcomes, review effort, latency, and total cost.

OpenAI Presence Guide: How Voice and Chat Agents Work for Business

Muawia Ali
-
0
OpenAI Presence Guide: How Voice and Chat Agents Work for Business

OpenAI Presence is an enterprise product for deploying voice and chat agents in customer-facing and internal workflows. OpenAI describes it as a managed deployment that combines model reasoning with company policies, permitted actions, guardrails, evaluations, and rules for escalation to people. It is available through a limited general availability program for eligible enterprise customers.

That description matters because Presence is easy to misread as a new button or mode inside ordinary ChatGPT. It is not a normal consumer ChatGPT feature, and OpenAI says it is not yet self-serve. An organization explores access through its OpenAI account team. OpenAI Forward Deployed Engineers and selected systems integrators lead deployments.

This guide is based on OpenAI’s official Presence announcement, published July 22, 2026. It explains what the company has documented, where the announcement uses promotional language, and which commercial and technical details remain unspecified.

OpenAI Presence deployment flow from a defined business job to systems, policies, evaluation, launch, and human escalation
Presence deployments begin with a defined job and controlled access, followed by evaluation, launch, observation, and approved updates.

What OpenAI Presence is

Presence is meant to help an enterprise put an agent into a specific production workflow. OpenAI gives examples such as resolving billing issues, supporting insurance claims, and handling employee IT service requests. The agent can answer questions, use connected company systems, take actions that the organization has approved, and transfer work to a person when required.

The product supports real-time voice and chat experiences. OpenAI lists customer support, outbound sales, and higher-risk internal workflows as current contexts. A billing agent, for example, might interpret a request, verify a customer, retrieve account information, apply the company’s policy, and perform an allowed action. This example describes the intended product pattern. It does not mean every Presence agent receives every capability automatically.

Each deployment starts with one job. According to OpenAI, the agent receives only the knowledge and system access needed for that job. The customer defines what it may do, which steps need approval, and when a human should take over. This narrow scope is central to the product’s design, not an optional prompt-writing technique.

How a deployment is assembled

OpenAI says its team works with the customer to identify a useful workflow, connect relevant knowledge and systems, set permissions and policies, test the agent, and move it into production. Selected systems integrators may also support the deployment as it expands. This is a service-led implementation rather than a software download that a team configures alone.

Presence brings several components together: policies and standard operating procedures, guardrails, approved actions, simulations, evaluation tools, and a Codex-powered process for proposing improvements. Companies can keep policies, evaluation methods, and escalation rules consistent while adapting other parts for a particular workflow or channel.

The announcement does not publish a standard connector catalog or promise compatibility with named CRM, contact-center, ticketing, identity, or payment products. It says Presence can connect company systems and use account context, but the specific integration work appears to depend on the deployment. A buyer should therefore ask which systems are supported for its use case, how authentication works, what data each connection exposes, and which actions are read-only or write-enabled.

Readers comparing this managed enterprise approach with other OpenAI products may find our guide to Codex for knowledge work useful. The products can involve Codex, but they are not interchangeable: Presence is described as an agent deployment product, while the linked guide covers a different work context.

Voice, chat, and human hand-offs

Voice and chat are channels for the same basic operating model. The organization decides which behavior should remain common across channels and what should change. A spoken interaction may require different pacing or verification from a chat session, but policies and escalation conditions can be shared where appropriate.

OpenAI explicitly documents escalation to people. Guardrails can intervene when an interaction moves outside company boundaries, and tests can check whether an agent escalated at the right time. Production escalations also become a signal that teams can examine after launch. This means a hand-off is not merely a generic fallback message. It is part of the behavior that the deployment team defines and evaluates.

The source does not specify the exact transfer experience. It does not say whether every deployment supports a warm transfer, a live transcript, a generated summary, queue routing, or continuity across channels. Those details should be confirmed during solution design rather than assumed from the phrase “escalate to people.”

Testing, oversight, and changes after launch

Before release, teams can run simulations covering common requests, edge cases, and higher-risk situations. OpenAI says graders assess whether the agent reached the right outcome, followed policy, used tools correctly, and escalated when appropriate. That is a clearer standard than measuring whether an answer merely sounded fluent.

Oversight continues in production. Sessions, escalations, and quality signals can expose cases where the agent performs well or needs attention. OpenAI says Codex, using a Presence plugin, investigates those signals and suggests updates. Teams can test a proposed change against the production version before approving a staged release.

This process still requires accountable human decisions. The announcement says teams test and approve proposed changes. It does not establish that Codex autonomously edits a live agent, nor does it describe approval roles, audit-log retention, rollback mechanics, or review frequency. Enterprises should request those operational details and decide who can approve policies, permissions, tools, and production releases.

A practical evaluation should use examples from the chosen job. Tests should cover correct outcomes, prohibited actions, incomplete verification, ambiguous requests, system failures, and the point at which a person must intervene. The source supports this kind of scenario testing, but it does not provide a universal acceptance threshold. Each organization must define what adequate performance means for its own risk and service requirements.

OpenAI Presence evaluation checklist covering outcomes, policy compliance, tool use, approvals, guardrails, and human hand-offs
A useful acceptance review checks results and boundaries, including whether tools, approvals, and escalation rules worked as intended.

What OpenAI reports from early deployments

OpenAI says Presence powers its English-language phone support line at 1-888-GPT-0090. The company reports that the agent handles open-ended requests, verifies callers, uses account context, and takes approved actions. It also says the system resolves 75 percent of inbound issues without human assistance and that its improvement process reduced human hand-offs by 15 percentage points in 10 days.

Those numbers are OpenAI’s own reported results for its support channel, not an independent benchmark or a guaranteed result for another company. The announcement does not publish the underlying sample, issue mix, full grading method, or confidence intervals. A prospective customer should not convert those figures into a business case without testing its own workflow and defining comparable measures.

OpenAI also identifies BBVA, SoftBank, and IAG as enterprises exploring or testing Presence-related experiences. The wording is important. BBVA is exploring voice support for everyday banking needs in Mexico, SoftBank is testing Japanese-language conversations, and IAG is exploring support during events such as severe weather. These examples show design-partner activity; the announcement does not establish broad production rollout or identical capabilities at all three companies.

Availability and purchasing context

Presence is available to eligible enterprise customers through limited general availability. OpenAI directs interested organizations to their account teams. It says deployments are led by Forward Deployed Engineers and selected global systems integrators, with further support possible as deployment expands.

No public self-serve signup, standard price, usage rate, minimum commitment, deployment schedule, or service-level agreement appears in the announcement. It also does not state which countries, industries, languages, call volumes, or enterprise account types qualify. These are sales and scoping questions for the account team.

OpenAI separately says it will continue supporting voice customers that access frontier models through the OpenAI API. That sentence helps distinguish Presence from building directly with APIs. Presence packages deployment systems and expert involvement around a defined enterprise agent. The API remains another route for teams that are building voice products. The announcement does not provide a feature-by-feature comparison or say that one route replaces the other.

What the announcement does not establish

The official post provides a product overview, not a full contract or technical specification. It does not publish pricing, service levels, uptime commitments, data residency options, retention periods, security certifications, regulatory coverage, model choices, capacity limits, latency targets, or a complete integration list. It also does not explain whether customers can export configurations or move a deployment between providers.

None of those omissions proves that a feature or assurance is unavailable. It means the cited source does not establish it. Security, privacy, legal, procurement, and architecture teams should review current contractual and technical material for the proposed deployment instead of filling gaps with assumptions.

Presence should also not be presented as a consumer ChatGPT agent builder. If you are researching self-contained ChatGPT creation tools, our ChatGPT Sites guide covers a separate product surface. Access to ChatGPT, Codex, the API, or another OpenAI business product does not, by itself, establish eligibility for Presence.

Questions to take to an OpenAI account team

Start with the exact job, channel, users, systems, and allowed actions. Ask how caller or user verification is performed, how the agent’s permissions are limited, and how a person receives an escalated case. Request the supported integration design for each required system rather than relying on a general claim that systems can be connected.

Then ask how simulations and graders are created, who can change policies, how releases are approved, and what evidence is retained. Commercial discussions should cover eligibility, implementation responsibilities, partner involvement, pricing, support, service commitments, data handling, and exit arrangements. These questions do not imply answers that OpenAI has not published. They turn the public overview into a concrete due-diligence process.

Frequently asked questions

Is OpenAI Presence a feature in regular ChatGPT?

No. OpenAI describes Presence as a deployed enterprise product in limited general availability. It is not self-serve, and interested organizations are told to contact their OpenAI account team.

Does Presence support both voice and chat agents?

Yes. OpenAI says the product currently supports real-time voice and chat experiences. The customer decides which policies, evaluations, and escalation rules stay consistent and what changes for each workflow or channel.

Can a Presence agent take actions in company systems?

OpenAI says an agent can use connected systems and take approved actions. Access is scoped to the job, while the company defines permissions, approval points, and hand-off rules. The announcement does not list every supported integration or action.

How can an organization get OpenAI Presence?

Eligible enterprise customers can access it through a limited general availability program. OpenAI says to contact the organization’s account team. The public announcement does not provide self-serve enrollment, standard pricing, or universal eligibility criteria.

Bottom line

Presence is best understood as a managed enterprise deployment for a narrowly defined voice or chat job. Its documented model combines limited knowledge and system access with company policies, testing, guardrails, approved actions, human escalation, and reviewed updates after launch. The official announcement establishes that operating model and the current access route. Detailed commercial, security, integration, and service commitments must come from the materials supplied for a specific deployment.

Finances in ChatGPT: Connecting Accounts, Privacy, and Practical Limits

Muawia Ali
-
0
Finances in ChatGPT: Connecting Accounts, Privacy, and Practical Limits

Finances in ChatGPT is a dedicated personal finance experience that can connect supported financial accounts, organize account data into a dashboard, and use that context when answering finance questions. It is not a system for moving money or making trades. It also does not turn ChatGPT into a financial professional.

Financial disclaimer: ChatGPT is not a fiduciary, registered investment adviser, broker-dealer, tax preparer, law firm, or substitute for a qualified professional. Its responses are for information and planning only and may contain mistakes. Check account data and calculations yourself. Consult an appropriately qualified professional before making a decision with legal, tax, credit, retirement, or investment consequences.

This guide follows OpenAI’s current documentation rather than relying on a third-party report. It explains who can use the feature, what happens during a Plaid connection, what ChatGPT can and cannot do, and how to remove connected data. Availability and behavior may change, so check the linked OpenAI pages before relying on any detail.

Who can use Finances in ChatGPT?

OpenAI’s current Help Center article says Finances is available to ChatGPT Plus and Pro users in the United States on the web, iOS, and Android. The rollout is gradual, so an eligible user may not see it immediately. The feature is not documented there as available to Free users, users outside the United States, or other plans and workspace types.

The distinction matters because the original May 15, 2026 announcement described a preview for Pro users in the United States, initially on web and iOS. OpenAI added an update dated June 25, 2026 saying the experience had expanded to Plus and Pro users in the United States on web, iOS, and Android. The Help Center reflects that newer eligibility. If the Finances entry is missing from your sidebar despite meeting those conditions, gradual rollout may be the reason.

OpenAI calls this experience a new personal finance experience in ChatGPT. Its separate Finances in ChatGPT Help Center guide contains the more detailed operating, privacy, removal, and troubleshooting information used below.

What the feature can show

Once supported accounts have synced, the Finances page provides a dedicated dashboard. What appears depends on the data your institutions make available and whether ChatGPT has enough information to generate a particular widget. OpenAI lists possible views for spending by category, current-month spending, upcoming payments, subscriptions, bills, net worth, portfolio distribution, and market updates for equity holdings.

The dashboard also shows connection status and the last sync time. A pencil icon lets you reorder widgets or choose which widgets appear. A missing card does not necessarily indicate an error. It may mean the connected institution did not provide the needed fields or that ChatGPT lacks enough data.

Finances in ChatGPT account connection and dashboard workflow

In chat, connected context can support questions about where money went, recurring charges, expected bills, spending trends, net worth, and portfolio allocation. ChatGPT may cite the relevant connected data so you can inspect the basis for an answer. You can ask questions from the Finances page or in a regular conversation by using /finances or @finances.

How the Plaid connection works

Open Finances from the ChatGPT sidebar and select Get started. You can also type @Finances, connect my accounts in a conversation. ChatGPT then offers Connect with Plaid. After that, sign in through the connection flow, choose the financial institution and accounts you want to connect, and return to ChatGPT.

Plaid maintains connections to more than 12,000 financial institutions, including supported banks, credit card issuers, brokerages, and other providers. That number does not mean every institution or every account type will work with Finances. An institution may be absent, temporarily unavailable, or unable to supply the particular data the feature needs.

After authentication, ChatGPT starts syncing and categorizing data. OpenAI says this usually takes a few minutes, although rare cases can take several hours. Syncing continues automatically. If an account needs attention, the Accounts tab may ask you to reconnect, update login information, or complete a security step with the institution.

Choose only the accounts and data you are comfortable sharing. According to OpenAI, ChatGPT can access balances, transactions, investments, and liabilities when those data are supplied through the connection. It cannot see full account numbers. The feature also cannot change an account, move money, or place an order.

Permissions and practical limits

Finances is an information and planning surface, not a financial transaction service. OpenAI says ChatGPT cannot move money, pay bills, change account settings, make trades, change retirement contributions, open or close financial accounts, or file taxes. It cannot act as your financial, legal, tax, or investment adviser.

Connected data can also be incomplete. One institution may provide balances without transaction history. Another may omit holdings, loan details, annual percentage rates, due dates, or other fields. Historical coverage is limited to what the institution supplies. ChatGPT cannot reconstruct missing records merely because an account connected successfully.

Classification introduces another source of error. Transfers, reimbursements, credit card payments, and duplicate pending transactions can be counted as spending or placed in the wrong category. Dashboard figures can also lag while an account is syncing or when an institution has not shared its latest data. Ask which transactions and fields were used, then compare the answer with the institution’s own records.

You can ask ChatGPT to reclassify a transaction or explain why a widget differs from your expectation. Treat that as a correction to the conversational context, not a change to the bank’s records. For unsupported property, private loans, planned purchases, or other manual details, you can describe the item in a finance conversation and decide whether to save it as a financial memory.

Financial memories and shared context

Account feeds do not capture every relevant fact. You might share that rent is divided with a partner, that a family loan remains outstanding, or that you want to keep a minimum balance in checking. ChatGPT can save such details as financial memories and use them in later finance conversations.

Financial memories are separate context intended for finance conversations. You can view or delete them from the Finances page through the three-dot menu. If you want a broader explanation of how saved context works, see our guide to ChatGPT memory and its controls. Remember that deleting a connected account and deleting a financial memory are different actions.

Privacy, training controls, and deleting data

Existing ChatGPT data controls apply to Finances. Conversations involving connected finance data follow the model training setting selected for the account. You can review or change that choice under Settings > Data controls. OpenAI does not describe financial conversations as automatically exempt from the training preference you have chosen.

Temporary Chat behaves differently. In a temporary chat, ChatGPT will not access connected financial accounts, use memories for personalization, or create memories. The temporary conversation also does not appear in chat history. This can be useful when you want a finance-related discussion that does not draw on the connected account context.

To disconnect Finances, use Settings > Apps > Finances or the Finances home page. The Accounts tab also lets you manage or remove individual connections. OpenAI says synced account data is deleted from its systems within 30 days after disconnection. Data associated with that ChatGPT connection is deleted from Plaid within 30 days under Plaid’s policies.

Disconnection does not remove financial information already present in your ChatGPT conversation history. Delete those conversations separately if you no longer want them retained in history. It also does not remove financial memories, which have their own controls. Finally, disconnecting Finances affects the ChatGPT connection only. It does not cancel other Plaid connections you may have authorized for unrelated apps.

Checklist for reviewing Finances in ChatGPT data, memories, and privacy controls

Useful questions to ask

Start with questions whose inputs you can verify. Ask, “Which transactions did you count as dining this month?” before asking for a spending comparison. For recurring costs, try, “List the subscriptions you found, including amount, cadence, and next expected charge date.” A bill review can begin with, “Which upcoming payments are based on confirmed recurring transactions, and which are estimates?”

For a cash-flow check, ask ChatGPT to separate account data, memories, and assumptions. You might request a comparison of this month’s grocery spending with recent months, then inspect any transfers or reimbursements included in the total. For debt, ask it to organize balances and interest rates, identify missing fields, and model several payment amounts without presenting one scenario as a recommendation.

Portfolio questions need similar care. Ask for the allocation shown by the available holdings and the date of the last sync. Do not assume the result includes every brokerage, private asset, retirement account, or current market price. A daily change shown in a widget is not a reason by itself to trade.

For tax topics, ChatGPT can organize information, explain concepts, and point out areas to review, such as estimated payments or possible deductions. OpenAI states that it does not file taxes and does not replace a tax professional. Do not submit a return or execute a tax-sensitive transaction solely from a generated response.

A careful review routine

  1. Check the last sync time and resolve any account marked as needing attention.
  2. Confirm which accounts are connected and whether an expected account is missing.
  3. Ask ChatGPT to identify the source data and assumptions behind important figures.
  4. Compare balances, transactions, holdings, dates, and calculations with original statements.
  5. Review financial memories and remove details that are outdated or no longer useful.
  6. Before disconnecting, delete any conversations and memories you also want removed.

Apply the same caution you would use with any sensitive ChatGPT connection. Our ChatGPT Health privacy-controls guide covers a different product, but its practical habit is relevant here: inspect permissions, understand what persists, and verify consequential information at the original source.

FAQ

Is Finances in ChatGPT available to every user?

No. OpenAI currently documents availability for Plus and Pro users in the United States on web, iOS, and Android. Rollout is gradual, so some eligible accounts may not see it immediately.

Can ChatGPT transfer money or trade from a connected account?

No. OpenAI says it cannot move money, pay bills, make trades, change retirement contributions, alter account settings, or open and close accounts. The connection supplies context for viewing, explanation, and planning.

What happens after I disconnect Finances?

OpenAI says synced account data is deleted from OpenAI’s systems within 30 days, and data associated with the ChatGPT connection is deleted from Plaid within 30 days under Plaid’s policies. Conversation history and financial memories require separate deletion.

Should I rely on a Finances answer for an investment or tax decision?

No. Review the underlying data and consult a qualified professional when a decision has investment, tax, legal, credit, or retirement consequences. ChatGPT may make mistakes and does not act as an adviser, tax preparer, fiduciary, broker-dealer, or law firm.

Bottom line

Finances in ChatGPT can place supported account data and personal context in one conversational workspace. Its value depends on the completeness and freshness of the connected data, careful memory management, and the user’s willingness to check every consequential answer. Connect selectively, inspect the source records, and keep financial decisions in human hands.

CHATGPT Custom Instructions Guide: How to Use the New 5,000-Character Limit

Muawia Ali
-
0
CHATGPT Custom Instructions Guide: How to Use the New 5,000-Character Limit

Custom instructions give ChatGPT a standing set of preferences to consider when it responds. They are useful for choices that should remain consistent across conversations, such as your preferred tone, reading level, units, formatting habits, or the kind of context you regularly need. On July 15, 2026, OpenAI increased the available space for some paid and organizational plans from 1,500 to 5,000 characters. The larger field makes room for clearer guidance, but it does not turn custom instructions into a private database or a guarantee that every response will follow every line.

This guide explains who receives the larger limit, what custom instructions affect, how to write them well, and what to consider before storing ongoing context. It is based on OpenAI’s current help documentation and release notes, not on personal product testing.

Who can use the 5,000-character limit?

OpenAI’s Custom Instructions help article lists two limits. Plus, Pro, Enterprise, Business, and Education users can save up to 5,000 characters. Free and Go users can save up to 1,500 characters. Custom instructions themselves are available on all plans across web, desktop, iOS, and Android; the difference is how much text each eligible account can store.

The July 15, 2026 release note describes the increase as a change from 1,500 to 5,000 characters for the five eligible plan groups. It says the added room is intended to support more customization of response style and behavior. It does not announce 5,000 characters for Free or Go accounts, so users on those plans should design within the documented 1,500-character allowance.

A character limit counts more than visible words. Spaces and punctuation also occupy space. You do not need to fill the field, and a shorter set of specific instructions is often easier to maintain than a long policy document.

What custom instructions change

OpenAI describes custom instructions as information you want ChatGPT to consider in its responses. That can cover stable preferences and recurring context. For example, you might ask for plain English, metric units, concise answers first, code examples in a particular language, or a short explanation of unfamiliar terms.

These instructions act as background guidance. They do not replace the request you type in a chat. A useful division is to keep durable preferences in custom instructions and put task-specific details in the current prompt. If you are writing one grant application, the grant rules belong in that conversation. If you always want dates written in ISO format, that may belong in custom instructions.

OpenAI says updates are applied immediately across chats, including existing conversations. In practice, that means subsequent responses can use the new version. Earlier messages are not rewritten. The help article also notes that previous versions may remain visible in chat history. Removing or editing the setting controls future responses, while clearing an old conversation is the documented way to remove old instruction text that appears in that conversation.

Workflow for separating permanent ChatGPT custom instructions from task-specific prompts

How to find and control the setting

Use the path documented for your platform rather than relying on an old screenshot. On web and desktop, OpenAI directs users to Settings, then Personalization. Make sure Enable customization is on and enter the text in the Custom Instructions field. On iOS and Android, open Settings, select Customize ChatGPT, turn on Enable customization, and enter the instructions in the same named field.

You can disable the feature without immediately deleting the saved text. On mobile, turn Enable customization off under Customize ChatGPT. On web and desktop, go through Personalization and Custom Instructions, then turn the setting off. OpenAI presents deletion as an optional separate step. If you only want to compare behavior temporarily, disabling first is less destructive than erasing a carefully edited set.

Design instructions around stable decisions

The extra space works best when it removes repeated setup rather than adding a biography. Start by listing decisions you make in many chats. Then keep only the ones that would still be useful a month from now.

A practical structure is:

  • Context: your role or audience, limited to details that materially change an answer.
  • Response preferences: tone, depth, units, language, accessibility needs, or preferred output format.
  • Working rules: how to handle uncertainty, assumptions, citations, calculations, or code.
  • Boundaries: habits you do not want, such as invented sources, unnecessary jargon, or a conclusion that repeats the introduction.

Write each line so that another person could tell whether it was followed. “Write well” gives little direction. “Use short paragraphs, define technical terms on first use, and put the answer before background” is easier to interpret. Avoid packing several unrelated requirements into one sentence. If two rules conflict, revise them instead of hoping the model will decide which one matters more.

A reusable example for everyday work

The following is an original example, not an official OpenAI template:

I work with a mixed technical and nontechnical audience. Start with a direct answer, then add the reasoning needed to act on it. Use plain English and short paragraphs. Define specialized terms the first time they appear. Use metric units and ISO dates. When information is uncertain, state the uncertainty and identify what would confirm it. Do not invent quotations, sources, test results, or product behavior. For recommendations, explain the main tradeoff instead of presenting one choice as universally best. Use tables only when they make comparison easier.

This example is compact because each sentence addresses a recurring choice. It does not include a current project name, client data, a temporary deadline, or the contents of a document. Those belong in the relevant conversation, where they can be removed from the standing context later.

Use the larger field without creating contradictions

Five thousand characters can support more detail, but length is not a quality score. Long instructions often fail for ordinary editorial reasons: the same idea appears twice, exceptions are scattered, or a new rule conflicts with an old one.

Group related requirements and put the most important ones first. Prefer a clear default with one necessary exception. For example, “Answer concisely by default; give a detailed derivation when I ask for one” is more coherent than separate commands demanding both maximum detail and maximum brevity.

Keep examples limited to cases that clarify a rule. Ten formatting samples can consume space while making the intended default less obvious. If a project needs extensive terminology, a long rubric, or many source documents, keep that material with the project or conversation instead of turning account-wide instructions into a storage area.

Privacy, data use, and sensitive permanent context

Custom instructions persist as account-level settings, so treat them as ongoing context rather than a disposable prompt. Do not store passwords, API keys, payment details, private medical records, confidential client material, or other information that does not need to be present in every chat.

OpenAI says custom instructions are not shared with viewers of a shared link. It also warns that when third-party plug-ins are used, the model may give plug-in developers relevant information from the instructions. OpenAI therefore advises using only plug-ins you trust and excluding information you would not want provided to those developers.

The help article says information from the use of custom instructions may be used to improve model performance and links to OpenAI’s training opt-out information. Review your account’s Data Controls if you do not want your content used for that purpose. The same help page confirms that custom instructions are included in a ChatGPT data export.

Deleting the setting and deleting the account are different actions. You can edit or delete instructions for future conversations at any time. If you delete your OpenAI account, OpenAI says associated custom-instruction data is deleted within 30 days as part of that process. It also notes that chat records may have been de-identified and disassociated from the account if you had not opted out of content use.

If you want a broader look at related personalization controls, read our guide to ChatGPT memory and its controls. For account-level privacy choices, our ChatGPT training opt-out guide covers that separate setting.

Checklist for reviewing ChatGPT custom instructions, privacy, and conflicting rules

What custom instructions cannot guarantee

Custom instructions shape responses, but they do not verify facts, create new product permissions, or make unsafe information appropriate to share. A line asking for citations does not prove that a citation is real. A request for accurate calculations does not remove the need to check consequential results. Review important output against reliable sources.

The feature is also separate from the API. OpenAI’s help article says there is no custom-instructions API and directs API developers to use system messages for a similar effect in the Chat Completions API. Do not assume that text saved in the ChatGPT interface automatically becomes configuration for an API application.

Custom instructions can also collide with a direct prompt or with each other. When the output becomes awkward, inspect the standing rules before blaming the current question. A requirement to avoid lists may conflict with a request for a checklist. A general preference for brief answers may be a poor fit for a detailed contract comparison. The current task still needs enough context to resolve exceptions.

How to revise your instructions

Review the setting when your role, audience, tools, or writing needs change. A simple revision process keeps the text useful:

  1. Save a private copy of the current text if you may want to restore it.
  2. Remove outdated project names, repeated rules, and context that no longer affects most chats.
  3. Turn vague preferences into observable instructions.
  4. Resolve pairs that ask for opposite behavior.
  5. Disable customization briefly if you need to compare responses without the standing guidance.
  6. After editing, use future responses to see whether the important defaults are clear. Revise the instruction itself rather than adding more exceptions to every prompt.

Do not treat one imperfect answer as proof that the entire setup needs another thousand characters. First check whether the relevant rule was specific, whether the current prompt supplied the needed facts, and whether another standing instruction competed with it.

Frequently asked questions

Does every ChatGPT plan get 5,000 characters?

No. OpenAI currently documents 5,000 characters for Plus, Pro, Enterprise, Business, and Education. Free and Go users can save up to 1,500 characters.

Do changes affect an existing conversation?

OpenAI says setting updates apply immediately across all chats, including existing conversations. That affects subsequent responses. It does not rewrite earlier messages, and previous instruction text may still appear in chat history.

Are custom instructions private when I share a chat link?

OpenAI says they are not shared with shared-link viewers. However, relevant information may be provided to third-party plug-in developers when you use their plug-ins, so avoid sensitive standing context and use only plug-ins you trust.

Should I use all 5,000 characters?

No. Use enough space to state stable, non-conflicting preferences. Keep temporary project details in the current conversation and remove rules that no longer improve typical responses.

A short final checklist

Confirm that your account has the limit you expect. Keep standing preferences separate from current-task details. Write rules that can be checked, remove contradictions, and avoid sensitive information that does not belong in every chat. Review Data Controls and plug-in access before saving account-wide context. Most importantly, revise the instructions when they stop matching the way you actually work.

ChatGPT Memory and Controls: A Practical Guide for 2026

Muawia Ali
-
0
ChatGPT Memory and Controls: A Practical Guide for 2026

ChatGPT Memory is no longer just a small list of facts that you explicitly ask the assistant to save. OpenAI’s current system can build a changing summary from useful context across chats, files, and connected apps, then use that context to personalize later answers. The same feature can be convenient and easy to misunderstand. Turning memory off, deleting a chat, clearing the memory summary, and disabling model training are separate actions with different results.

This guide explains the controls OpenAI documents in July 2026. It is based on OpenAI’s help pages and release notes, not personal account testing. The interface can vary while updates roll out, so use the labels available in your own Settings page and avoid assuming that another user’s screen will match yours.

What ChatGPT Memory means in 2026

When memory is enabled, ChatGPT may use information from your chats, uploaded files, and connected apps to make future replies more relevant. OpenAI describes the current system as a continually updated synthesis rather than a complete transcript or a fixed notebook. You can review a memory summary, correct it, and delete the memories shown there. The summary can say when it was last updated, but it may not display every detail that affects personalization.

This current experience differs from the legacy saved memories system. Saved memories are individual details that you directly ask ChatGPT to remember, or that ChatGPT saves because they may help in later conversations. They are stored separately from chat history and remain part of future context until removed. The newer memory summary updates more automatically and is meant to avoid the stale or contradictory items that can accumulate in a manual list.

OpenAI still documents a way to return to legacy saved memories through Settings > Memory > Saved memories. The exact controls you see depend on whether the updated experience has reached your account. According to the ChatGPT release notes, memory summary updates were still rolling out across plans and countries in June 2026. That is the safest way to describe availability: documented rollout, not universal visual parity.

Diagram comparing ChatGPT memory summary, saved memories, and past chat sources
Memory can draw on several sources, while each source still has its own deletion and privacy controls.

Memory summary, saved memories, and past chats are different

The memory summary is a high-level view of what ChatGPT currently considers useful about you. It changes as you keep chatting. OpenAI says the summary should contain the most important details, but it is not a complete ledger. If you want to check whether a particular detail has been remembered, OpenAI suggests asking ChatGPT directly.

Legacy saved memories behave more like persistent notes. You might say, “Remember that I prefer vegetarian recipes,” and ChatGPT can consider that preference later. OpenAI says ChatGPT may also save useful details without an explicit request. You can delete an item, clear all saved memories, or turn the feature off. Deleting the chat where a saved memory began does not necessarily delete that memory because the two are stored separately.

Past chat context is broader. In the updated experience, ChatGPT can synthesize useful information from conversation history rather than relying only on a short list of saved items. This does not mean every past message is inserted into every new prompt, nor does the memory summary show every factor. It means relevant context from previous conversations may influence a response when memory is enabled.

Chat history itself remains a separate account record. You can search and reopen old conversations using the current interface described in our ChatGPT unified search guide. Finding a chat, deleting a chat, and changing memory are three different operations.

How to review and correct what ChatGPT remembers

Open Settings, choose Personalization, then open Memory. OpenAI’s Memory FAQ documents the memory summary and its management options. You can type a requested change into the box below the summary, or highlight text in the summary and make a specific correction. If the summary is empty, the account may be new, memory may have been enabled recently, or there may not yet be enough history. A Refresh option is available from the summary’s menu.

Corrections matter because personalization can be based on context that has changed. A location, work role, diet, current project, or preference may no longer be accurate. State the correction plainly and then review the summary. Do not treat a fluent response as proof that every stored detail has been changed.

OpenAI also provides source visibility beneath personalized responses. Tap the book icon below a response to see sources such as custom instructions, past chats, files, and memories. Selecting a memory can explain why it was used, and the three-dot menu can offer a correction route. OpenAI cautions that this source view may not show every factor that shaped the answer. Memory sources are not included when you share a chat.

Deleting a memory is not the same as deleting a chat

If you delete a chat, a legacy saved memory created from that conversation may still remain. If you delete a saved memory, the old chat can still contain the original statement and earlier replies that mention it. OpenAI says deleted saved memories may be retained in a log for up to 30 days for safety and debugging. Removing a memory also does not rewrite text that already exists in your conversation history.

The current memory summary adds another layer. The “Delete and turn off memory” option deletes the memories shown on the summary page and disables memory, but it does not delete past chats. If you enable memory again later, ChatGPT may form new memories from conversations that still exist, including older chats. That control was available on the web and rolling out on mobile when OpenAI documented it in June 2026.

For a more complete removal, delete every source where the information appears. OpenAI lists past chats, archived chats, files, the memory summary, and connected apps that contain the detail. Disconnecting an app prevents an ongoing connection, while deleting relevant stored material addresses information already present in your account. Check all of these surfaces rather than relying on one toggle.

Turning off training does not turn off Memory

Data Controls govern whether conversations help improve OpenAI’s models. Memory governs personalization. They are related to your data, but they are not the same setting. Under Settings > Data Controls, turning off “Improve the model for everyone” keeps regular conversations in your history while preventing them from being used to train ChatGPT. OpenAI says this preference applies across devices for the account.

Turning off model training does not automatically clear memory or stop past context from personalizing replies. Turning off memory does not necessarily delete chats or change the model-training preference. Review both sections when your goal involves both personalization and training. OpenAI’s Data Controls FAQ is the current source for the training toggle, account export, and related controls.

Custom Instructions are separate again. They are explicit directions about what ChatGPT should know or how it should answer. OpenAI says Temporary Chat still follows enabled custom instructions, even though it does not use or create memories. Review custom instructions if you want a conversation with neither remembered personal context nor standing instructions.

Checklist for reviewing ChatGPT memory, chat history, training, and Temporary Chat controls
A privacy review should check each control separately because one switch does not clear every source.

When to use Temporary Chat

Temporary Chat is the clearest option when you do not want a conversation to use existing memories or create new ones. Start a new chat and select the pill-shaped Temporary button in the upper-right corner, as documented in OpenAI’s Temporary Chat FAQ. Temporary chats do not appear in chat history and are not used to improve OpenAI’s models.

Temporary does not mean that no copy can exist. OpenAI may retain a copy for up to 30 days for safety purposes, and limited safety-relevant context may still be used in rare, high-risk situations. If you use a custom GPT with actions, information sent to an outside service is governed by that recipient’s privacy policy and may be kept longer. Temporary Chat cannot override a third party’s data handling.

Use Temporary Chat for a one-off discussion that should not personalize later answers. It is not a reason to paste secrets, medical records, financial credentials, workplace data, or another person’s private information without authorization. The safer rule is to minimize sensitive details before sending them at all.

Sensitive information and practical privacy limits

OpenAI says sensitive information may appear in memory if you share it with ChatGPT. The product controls reduce unwanted reuse, but they do not convert unnecessary disclosure into a good practice. Remove names, account numbers, addresses, passwords, access tokens, health identifiers, and confidential business details when they are not needed for the task.

Connected apps and uploaded files deserve the same attention as typed messages. A memory may be influenced by a file or app even if the detail is not obvious in the visible chat. The source icon can improve transparency, but OpenAI says it may not show every source or factor. If something must be fully removed, inspect the original file, app connection, archived conversations, and memory summary.

For project-based work, keep persistent reference material in a clearly scoped location rather than expecting general memory to act as a document archive. Our guide to ChatGPT project sources explains that separate workflow. Memory is for personalization context, not a guaranteed record of every fact or file.

A short control review

  1. Open Settings > Personalization > Memory and review the summary available to your account.
  2. Correct details that are outdated or wrong, then ask whether the specific fact is still remembered.
  3. Use the book icon under a personalized response to inspect the sources OpenAI exposes.
  4. Delete both the memory and the original chats, files, or app sources when complete removal is the goal.
  5. Review Data Controls separately and set “Improve the model for everyone” according to your preference.
  6. Use Temporary Chat when a conversation should neither access nor create personalization memories.
  7. Check custom instructions too, since Temporary Chat can still follow them.

Frequently asked questions

Does deleting a chat delete the memory it created?

Not necessarily. Saved memories are stored separately from chat history. Delete the saved memory or summary entry as well as the chat, and remove any other file, archived chat, or connected-app source that contains the same information.

Can I see everything ChatGPT remembers about me?

The memory summary gives a high-level view, but OpenAI says it may not contain every detail or factor used for personalization. You can ask ChatGPT whether it remembers a specific fact and inspect the book icon under a personalized response.

Does turning off “Improve the model for everyone” disable Memory?

No. The Data Controls setting governs model training, while Memory governs personalization. A regular chat can remain in history and still participate in personalization even when it is not used for training, depending on your memory settings.

Is Temporary Chat completely unrecorded?

No. It does not appear in history, use or create personalization memories, or train OpenAI’s models, but OpenAI may keep a copy for up to 30 days for safety. Data sent through a GPT action may follow a third party’s separate retention policy.

What to remember

ChatGPT now combines a current memory summary with context from past conversations, while the legacy saved memories view remains available for accounts that use it. The controls are useful only when you distinguish their jobs. Memory changes personalization. Deleting chats changes history. Data Controls change training preferences. Temporary Chat avoids personalization memory for one conversation, subject to documented safety retention and third-party limits.

ChatGPT Desktop App for Mac: Chat, Work, Codex, Voice, and App Access

Muawia Ali
-
0
ChatGPT Desktop App for Mac: Chat, Work, Codex, Voice, and App Access

OpenAI now offers two ChatGPT applications for Mac, and the names matter. The new ChatGPT desktop app, released globally for macOS and Windows in July 2026, brings Chat, Work, and Codex into one application. The earlier Mac application has been renamed ChatGPT Classic. It remains supported, but OpenAI directs people who want agentic Work and Codex features to the new desktop app.

This guide explains the current product split, installation requirements, the Option+Space shortcut, desktop app access, voice, privacy, and review habits. It is based on OpenAI’s published documentation and release notes. It does not claim personal testing, and it does not assume that a feature or usage allowance is available on a particular plan unless the cited documentation says so.

Choose the right ChatGPT app for your Mac

The new desktop app has a global switcher between ChatGPT and Codex. Inside ChatGPT, Chat handles questions and conversational help, while Work is intended for longer tasks that can produce finished deliverables. Codex keeps its own view and software development workflows. Chat and Work conversations appear together in Recents, and existing ChatGPT Projects are available in the desktop app.

ChatGPT Classic is the previous macOS app under a new name. OpenAI says no migration was required when it was renamed and that it continues to receive model updates, bug fixes, security patches, and support for its existing Enterprise capabilities. New agent features such as Work and Codex belong to the new app. If you already used the Codex app, updating it moves you to the new combined desktop app. If you used the previous ChatGPT app, OpenAI says to follow its prompt to download the new version. Classic may remain installed.

This distinction prevents a common source of confusion. An older help page may describe a companion window or the original Work with Apps interface, while the July 2026 release notes describe the combined Chat, Work, and Codex application. Check which application you have before following interface instructions.

System requirements and safe installation

The current minimum requirement is macOS 14 on either Apple Silicon, starting with M1, or an Intel processor. OpenAI’s download page detects the processor and supplies the appropriate installer. Download from OpenAI’s ChatGPT download page rather than a third-party software directory. After installation, open the application from Finder and sign in or create an account.

If an older copy is already installed, look at its displayed name. ChatGPT Classic is not the same product surface as the new combined desktop app. Keeping both can help while you confirm your required workflows, but OpenAI says no migration was required at the Classic rename. For background on an earlier certificate change, see our ChatGPT Mac app security update guide.

ChatGPT desktop app workflow on macOS across Chat, Work, and Codex
Choose Chat for conversation, Work for longer tasks, or Codex for software development inside the new desktop app.

Use Option+Space without inventing extra shortcuts

Option+Space is the documented shortcut for opening ChatGPT quickly on macOS. OpenAI’s Work with Apps instructions say it opens the Chat Bar, and the menu bar icon provides another route. In the macOS app settings, the access shortcut can be changed under ChatGPT > Settings > Keyboard Shortcut.

The exact behavior can depend on the app generation and its current window state. Earlier macOS release notes describe Option+Space as starting a new chat, returning focus to an open companion window, or reopening the last companion window. The safest current advice is simple: use Option+Space as the documented quick access command, then follow the controls shown in your installed app. This guide does not treat older release-note shortcuts as promises about every current surface.

For routine use, the shortcut works best as an entry point. Open ChatGPT, state the task, attach or select only the context needed, and inspect the response or proposed action before accepting it.

Chat, Work, and Codex serve different jobs

Chat is suitable for quick questions, explanations, drafting, and conversation. Work is for tasks that may research information, use connected material, and create documents, spreadsheets, presentations, reports, or Sites. The desktop app can let Work use local files and desktop apps with your permission. Its built-in browser can gather information from websites and work with supported web tools and files.

Codex is the software development view. OpenAI’s July release notes describe inline editing in diffs, pull-request review in a side panel, faster computer use, and multiple repositories in one project. Those are Codex capabilities, not generic promises about every Chat conversation.

Context location also matters. Cloud Work conversations sync across web, mobile, and desktop, so you can continue them on another device. Local conversations stay on the computer. OpenAI documents that distinction, but it should not be stretched into a blanket claim that all processing is local. If a task contains sensitive material, confirm whether you are starting local or cloud Work and review the relevant workspace policies before adding files.

How Work with Apps handles Mac content

OpenAI separately documents Work with Apps for ChatGPT on macOS. You can connect an active compatible app from the Chat Bar or use the Work with Apps button. A banner identifies the apps ChatGPT is working with and the content it recognizes. If the banner is absent, the feature is not adding app content to that message. When you send, the recognized content and any selection are included with the message, and the chat shows what ChatGPT examined.

Supported text tools listed by OpenAI include Apple Notes, Notion, TextEdit, and Quip. Coding support includes Xcode, Script Editor, Visual Studio Code and several related builds, plus a long list of JetBrains IDEs. Supported terminals include Terminal, iTerm, Warp, and Prompt. The definitive compatibility list is available inside ChatGPT > Settings > Work with Apps > Manage Apps, which is more useful than assuming that any open Mac application will work.

The amount of context is limited. For terminal panes, ChatGPT includes the last 200 lines of open panes. If you select text in a pane, it focuses on that selection and adds neighboring text up to a truncation limit. Editing is available only with IDEs. For an IDE edit, ChatGPT can generate a diff for review and application, with an option to apply it automatically. OpenAI says changes can be reverted in the ChatGPT interface or with Command+Z in the editor.

Permissions, extensions, and admin controls

Most compatible apps require macOS Accessibility permission so ChatGPT can query their content. You can remove that access in System Settings by disabling ChatGPT’s Accessibility permission. Visual Studio Code requires the extension named ChatGPT – Work with Code on macOS. Manage Apps shows what each integration needs.

You can turn off the entire feature with Enable Work with Apps in ChatGPT settings. Enterprise administrators can also disable Work with Apps for workspace members, and an admin setting can disable code edits on macOS. If an expected control is missing on an Enterprise or Education account, an administrator may have restricted it. That is a better first check than repeatedly reinstalling the app.

Permissions should match the task. Do not leave an unrelated note, terminal pane, repository, or document selected merely because the connection is convenient. Before sending, read the app banner and narrow the selection. For code changes, review the diff and run the project’s normal tests rather than accepting an edit solely because it applies cleanly.

Review checklist for ChatGPT Work with Apps permissions, context, diffs, and privacy
Check the connected app, included context, requested action, and final output before you send or apply a change.

Voice in the desktop app

Voice now exists in more than one desktop context. OpenAI announced on July 23, 2026 that ChatGPT Voice is available in Work and Codex in the new desktop app. You start a new task in Voice, speak naturally, interrupt, and ask Voice to start or coordinate work using the tools and permissions available to the selected experience.

The Work with Apps help page also documents Advanced Voice in the Mac app. It starts from the wave icon in the main window’s chat bar and requires microphone access. During an Advanced Voice session, the Work with Apps button controls which apps are in context. That page states two limits: Advanced Voice cannot yet be launched from the companion window, and voice mode does not support code edits.

These statements address different interfaces, so they should be read together rather than collapsed into a broader promise. Voice can initiate or coordinate tasks in Work and Codex, while the documented Work with Apps voice flow still excludes code edits. Check the visible mode, app permissions, and proposed action before relying on speech for a desktop task.

Privacy, chat history, and review boundaries

Content sent through Work with Apps becomes part of the chat history and is saved to the account until the chat is deleted. OpenAI says deleted chats or a deleted account are removed from its systems within 30 days, unless the content was already de-identified and disassociated from the account or must be retained for security or legal reasons.

For consumer accounts, the Improve the model for everyone control determines whether conversations may be used to improve models. Temporary Chat is not saved in the ChatGPT account and is not used to improve OpenAI’s models. OpenAI also states that content submitted through its business offerings is not used to improve model performance. Workspace rules can still affect feature access and retention, so business users should follow their administrator’s policy.

Local versus cloud Work is a continuity choice as well as a context choice. Local conversations remain on the computer; cloud Work conversations sync across supported surfaces. Neither label excuses careless input. Remove secrets, credentials, personal records, and unrelated customer information unless the task and organization explicitly permit them. Our guide to ChatGPT memory and controls covers related account settings.

ChatGPT output also needs human review. Check cited sources, calculations, file changes, and external actions. For code, inspect the diff and test it. For a report, confirm that claims match the supplied material. For a message, verify the recipient and attachments. OpenAI’s documentation describes controls and capabilities, not a guarantee that every response or proposed edit is correct.

A practical desktop routine

  1. Confirm whether you are using the new ChatGPT desktop app or ChatGPT Classic.
  2. Choose Chat, Work, or Codex according to the task rather than treating them as interchangeable.
  3. Open the app with Option+Space or the menu bar icon.
  4. Check whether the task is local or cloud Work when that choice is offered.
  5. Connect only the file or supported app needed for the current request.
  6. Read the Work with Apps banner before sending and remove irrelevant context.
  7. Review diffs, citations, calculations, and actions before approval.
  8. Delete the chat or adjust data controls when the retained context is no longer appropriate.

Official sources

FAQ

Is ChatGPT Classic being discontinued?

OpenAI says ChatGPT Classic continues to be supported. It receives model updates, bug fixes, security patches, and support for existing Enterprise capabilities. Work and Codex agent features are in the new desktop app.

What Mac is required for the new ChatGPT desktop app?

You need macOS 14 and either Apple Silicon starting with M1 or an Intel processor. OpenAI’s download page detects the processor and provides the matching installer.

Does Work with Apps read every open application?

No. OpenAI lists specific compatible text editors, IDEs, and terminals. The Chat Bar banner shows which connected apps and recognized content will accompany a message. Manage Apps shows the current compatibility list and permission requirements.

Can Voice edit code while working with a Mac app?

The Work with Apps documentation says voice mode does not support code edits. Separately, Voice is available in Work and Codex for starting or coordinating tasks with the tools and permissions of the selected experience. Review the mode and any proposed changes before approval.

ChatGPT Using Too Much RAM? An Evidence-Conscious Troubleshooting Guide

Muawia Ali
-
0
ChatGPT Using Too Much RAM? An Evidence-Conscious Troubleshooting Guide

ChatGPT Using Too Much RAM? An Evidence-Conscious Troubleshooting Guide

A claim that ChatGPT consumed 163GB of RAM before crashing is alarming, but a large number does not establish a product-wide memory leak. It describes one reported outcome on one setup. It does not, by itself, identify which ChatGPT client was involved, whether the operating system counted compressed memory or swap, how long the session ran, what other processes were active, or whether an extension, browser tab, uploaded file, long conversation, or local integration contributed.

Most importantly, the specified official OpenAI sources do not confirm a general ChatGPT defect that makes the product consume 163GB of RAM. OpenAI’s troubleshooting documentation does acknowledge that ChatGPT can become slow, freeze, load endlessly, or stop responding. Its recommended response is methodical: check service status, restart or refresh, test a clean environment, change one variable at a time, and collect diagnostic evidence if the problem persists.

This guide follows that evidence. It will help you contain a runaway session, determine whether the problem is local or service-side, preserve useful observations, and report a reproducible case. It will not turn a dramatic anecdote into a diagnosis that the official record does not support.

What the 163GB story can and cannot tell us

“ChatGPT used 163GB” sounds precise, yet precision is not the same as completeness. A useful diagnosis needs the application name and version, operating system version, time of occurrence, workload, conversation length, attachment types, browser and extension state, and the exact metric being read. A screenshot taken after a crash may document an extreme state while revealing little about the path that produced it.

The phrase memory leak also has a technical meaning. It generally refers to memory that software allocates and fails to release when it is no longer needed, causing usage to grow over time. High RAM use can resemble a leak without proving one. A long page, many rendered messages, image previews, active tabs, browser extensions, cached resources, or a malfunctioning local process can all produce pressure. The key test is repeatability under controlled conditions, not the shock value of a peak reading.

OpenAI’s official troubleshooting page groups slow, lagging, frozen, and unresponsive behavior together and recommends starting a new chat when a conversation is long or has many turns. That recommendation supports a practical test: compare the troubled conversation with a fresh one. It does not establish that long chats cause a universal memory leak, nor does it validate the 163GB figure.

First response when memory use is climbing

If your computer is becoming unresponsive, preserve the machine before preserving the chat. Stop sending new prompts, pause uploads, and close other nonessential work. If you can still interact with the session, copy any unsaved prompt or response you need. Then stop the generation, close the affected tab or app, and restart it. If the whole system is close to locking up, ending the affected process may be the safest available action.

Do not keep a runaway process open merely to get a more dramatic screenshot. One timestamped capture of the process list and memory graph is more useful than allowing the computer to crash. Record the following before restarting, when feasible:

  • The local date and time, including time zone.
  • Whether you used the web client, the current desktop app, or ChatGPT Classic on macOS.
  • The browser or app version and operating system version.
  • The conversation URL or ID, without posting it publicly.
  • The selected model and whether a response was streaming.
  • Approximate conversation length and whether files, images, data analysis, Projects, Canvas, voice, or app integrations were in use.
  • The memory value, the process to which it was attributed, and whether the value fell after closing the session.

After the restart, resist the urge to reopen everything at once. A controlled recovery produces better evidence. Open only ChatGPT, start a new conversation, submit a small plain-text prompt, and observe the process for several minutes. If normal use is stable, add back one part of the original workload at a time.

ChatGPT high RAM incident worksheet with timestamps, client version, conversation state, memory readings, and reproduction steps
Capture a small set of comparable observations instead of relying on one peak number.

Check OpenAI status before changing your computer

OpenAI’s troubleshooting guide repeatedly directs users to the official OpenAI status page when ChatGPT is slow, stuck, or generating errors. Check it near the time of the incident and note any active ChatGPT event. The page cautions that availability is reported in aggregate across tiers, models, and error types, so an all-clear summary does not prove that every individual session is healthy. Conversely, an active service incident does not prove that local RAM growth has the same cause.

This distinction prevents two common mistakes. The first is spending an hour clearing caches during a known service disruption. The second is blaming an outage for behavior that reproduces only in one browser profile or on one machine. Status is a triage signal, not a complete root-cause report.

If there is an active incident, preserve your timestamp and wait for recovery before running aggressive local experiments. If there is no relevant incident, continue with isolation tests. Readers comparing several assistants for reliability may also find the site’s source-checked AI tools comparison useful, especially its emphasis on verifying product claims rather than trusting a single generated answer.

Run a clean-client isolation test

OpenAI recommends refreshing the page or restarting the app, signing out and back in, trying a private browsing window, disabling extensions and content blockers, clearing site data or cookies, and testing a different browser, device, or network. Those steps are often presented as fixes, but they are also diagnostic branches. Each branch asks whether the symptom follows the account, conversation, client, profile, device, or network.

  1. Restart the affected client. Force reload the web page or restart the desktop app. Test a new, short conversation before opening the original one.
  2. Try a private window. A private session reduces interference from the normal profile’s extensions and stored site state. Sign in, use a new chat, and repeat the same small prompt.
  3. Disable extensions and filters. OpenAI specifically identifies browser extensions, content blockers, VPNs, proxies, and secure DNS tools as possible sources of trouble. Disable them temporarily, provided doing so is permitted and safe in your environment.
  4. Try another browser or device. If the same account and prompt behave normally elsewhere, the original client or machine becomes a stronger suspect. If the issue follows you, record that result rather than declaring the local machine innocent.
  5. Try another network only if relevant. Network changes are useful for connection errors, websocket failures, or endless loading. They are less direct evidence about local memory, so do not overinterpret them.

Use a simple test matrix. Keep the prompt constant, run each condition for a similar duration, and record starting, peak, and post-close memory. “It seemed better” is weaker than “the clean profile remained stable through three five-minute trials while the normal profile climbed each time.” You do not need laboratory perfection. You need enough consistency to separate a repeatable pattern from a one-off failure.

Separate conversation scope from client scope

A fresh chat is one of OpenAI’s explicit suggestions for a long or many-turn conversation. Test that advice directly. First run a short prompt in a new chat. Then, only if the machine remains responsive, open the conversation associated with the incident without immediately generating a response. Finally, try a comparable prompt in another new chat.

If memory rises only when the old conversation is displayed, document that. If it rises only while a particular response streams, document that instead. If every blank session causes the same growth, the conversation itself is less likely to be the only factor. Avoid repeatedly reopening a session that destabilizes the computer. One or two controlled reproductions are generally more responsible than chasing an exact peak.

Attachments deserve their own branch. Note whether the conversation contains large documents, images, generated files, charts, or data analysis. Test plain text first, then a small non-sensitive attachment if necessary. Never upload confidential material solely to reproduce a bug. A useful report can state that the symptom appeared with a file and disappeared without exposing the file publicly.

Decision tree for isolating ChatGPT high RAM use across service status, browser profile, desktop app, conversation, and attachments
Change one condition at a time so each result narrows the problem rather than adding noise.

macOS app context and why version details matter

The official macOS release notes show that the client has changed over time. OpenAI records fixes for unnecessary network usage, crashes, interface problems, and performance in launching, scrolling, streaming responses, rendering, and data analysis. The notes also say the previous macOS app became ChatGPT Classic on July 9, 2026, while users could continue using it without migrating at launch.

None of those entries confirms the specific 163GB allegation. They do show why “the ChatGPT app” is not enough detail for a support case. State whether you used ChatGPT Classic or the newer desktop app, then include the visible version information. OpenAI’s release notes have previously told users to check for updates and restart to receive improvements, so updating and restarting is a reasonable controlled step after you capture the original version.

Do not use a generic release note such as “fixes and improvements” as proof that your memory problem was fixed. Test before and after under the same modest workload. If an update changes the result, report the old version, new version, and comparison. If it does not, that negative result is still useful.

For broader purchasing and workflow decisions, the guide to choosing AI apps beyond ChatGPT offers a practical reminder: select tools by the job and constraints, not by viral claims. The same principle applies here. Choose the web or desktop client based on observed stability in your actual workflow, not an assumption that one interface must always use less memory.

Protect your chat history while troubleshooting

Restarting a client and deleting a chat are different actions. According to OpenAI’s official retention policy, chats remain saved to your account until you delete them manually. Deleting a chat removes it from your account immediately and schedules permanent deletion from OpenAI systems within 30 days, subject to stated de-identification and legal or security exceptions. Deleted chats cannot be recovered.

That means deletion should not be your first reflex. If you simply need to remove a conversation from the sidebar while retaining it, OpenAI says archiving is the appropriate option, and archived chats follow the same retention rules as unarchived chats. If the conversation itself appears to trigger the problem, capture its URL or ID for private support use, save only the non-sensitive information you are authorized to retain, and decide deliberately whether to archive or delete it.

Temporary Chats are handled differently. OpenAI says they are automatically deleted from its systems within 30 days. Files also require care. The official policy says files saved to Library are managed separately from chats, so deleting a chat does not delete files saved to Library. Files attached to a custom GPT or project are retained until that GPT or project is deleted, then removed within 30 days unless an applicable legal or security exception requires longer retention.

These policies matter because a panicked cleanup can destroy evidence without removing every associated file. Before deleting anything, decide whether you need a support reference, whether a Library file must be removed separately, and whether workplace retention rules apply.

Build a support report that can be investigated

OpenAI’s troubleshooting page asks users with persistent problems across browsers, devices, and networks to collect a HAR file and browser console errors with timestamps, note the model and conversation URL or ID, and contact Support. For blank, frozen, or endlessly loading pages, it similarly recommends console logs and a HAR file with timestamps.

A HAR file can contain sensitive request details. Treat it as diagnostic data, inspect and sanitize it according to the official support instructions, and share it only through the appropriate support channel. Do not attach it to a public forum post. The same caution applies to screenshots that reveal conversation titles, prompts, account details, or filenames.

A concise report should include:

  • A one-sentence symptom description without asserting an unproven cause.
  • Exact local timestamps and time zone.
  • Client name, client version, browser profile state, and operating system version.
  • Model, conversation URL or ID, and whether the chat was long or attachment-heavy.
  • Observed memory at baseline, peak, after closing the chat, and after restarting the client.
  • Reproduction steps with one change per test.
  • Results from a fresh chat, private window, extension-free profile, other browser, and other device when available.
  • Whether the official status page showed an incident.
  • Relevant console errors and a carefully handled HAR file.

Phrase the conclusion conservatively: “The desktop process rose from approximately X to Y during this sequence and returned to Z after restart” is observable. “ChatGPT has a universal memory leak” is not established by one machine. This wording helps support focus on the behavior rather than first having to untangle an inflated claim.

A practical stop rule

Troubleshooting should have a stopping point. Stop local reproduction if the system becomes unstable, if testing risks unsaved work, or if the next step would require exposing sensitive data. Use the cleanest stable client available, split very long work into new conversations when practical, keep software updated, and retain your report for Support.

If the issue disappears after a restart, log it anyway, but do not call it solved forever. If it returns under the same conditions, your earlier notes provide a baseline. If it persists across clean profiles, multiple clients, and devices, escalate with the evidence OpenAI requests. If it occurs only in one environment, focus further testing there.

The responsible conclusion is straightforward. A 163GB peak deserves attention, but it does not deserve certainty that the evidence cannot provide. Official OpenAI guidance supports systematic troubleshooting for slow, frozen, and unresponsive ChatGPT sessions. It does not confirm that the anecdote represents a product-wide defect. Measure, isolate, protect your data, and report what you can reproduce.

Frequently asked questions

Is the 163GB ChatGPT memory leak officially confirmed?

No. None of the specified official OpenAI sources confirms a general defect that causes ChatGPT to consume 163GB of RAM. The number should be treated as an anecdotal observation unless OpenAI publishes a finding or a controlled reproduction establishes the behavior on a defined client and version.

Should I delete the affected conversation?

Not immediately. Restart the client and test a new chat first. OpenAI says deleted chats cannot be recovered, while archiving hides a chat without deleting it. If you may contact Support, preserve the conversation URL or ID privately before making a deletion decision. Remember that Library files are managed separately from chats.

What is the fastest useful test?

Check the official status page, restart the browser or app, and run a short plain-text prompt in a new chat. Then compare it with a private browser window or clean profile. Record memory before, during, and after the test. This quickly distinguishes a transient session problem from behavior tied to a profile or client.

When should I contact OpenAI Support?

Contact Support when the issue persists after restart and reproduces across clean browser conditions, devices, or networks, or when the desktop app repeatedly becomes unstable. Include timestamps, model, client and version, conversation URL or ID, controlled reproduction steps, and the diagnostic material requested in OpenAI’s troubleshooting guide.

Official sources

5 AI Tools for Everyday Work: A Source-Checked Comparison

Muawia Ali
-
0
5 AI Tools for Everyday Work: A Source-Checked Comparison

Choosing an AI assistant is easier when the question is specific. Do you need a flexible general-purpose workspace, careful help with long documents, close ties to Google services, a web research tool that shows its sources, or a notebook that answers from material you select?

This comparison covers exactly five products: ChatGPT, Claude, Gemini, Perplexity, and NotebookLM. It is based on official product, help, and privacy documentation, not personal testing. Features and limits can vary by plan, account type, region, and date, so check the linked documentation before paying or uploading sensitive material.

Decision map comparing ChatGPT, Claude, Gemini, Perplexity, and NotebookLM by everyday task

Quick comparison

Tool Good fit for Main caution
ChatGPT General writing, explanation, file work, images, and coding in one interface Capabilities and data controls differ across plans and workspaces
Claude Reading, drafting, analysis, and sustained work with documents Users still need to verify claims, quotations, and calculations
Gemini People who want an assistant connected to the Google ecosystem Connected apps, availability, and data handling depend on settings and account type
Perplexity Web research where visible citations are central to the workflow A citation can be present but still fail to support the sentence beside it
NotebookLM Questions, notes, and summaries grounded in a chosen source collection Its answers inherit omissions and errors from the uploaded source set

1. ChatGPT: the broad general-purpose option

OpenAI presents ChatGPT as a place for everyday questions, learning, drafting, web search, image generation, file work, and coding. That breadth is its clearest reason for consideration. A person who regularly switches between rewriting an email, explaining a concept, examining a spreadsheet, and sketching code may prefer one adaptable interface over several narrow tools.

The trade-off is that “ChatGPT” does not describe one fixed experience. Available models, tools, connectors, message allowances, and administrative controls can differ by plan. A workflow that depends on a particular model or connected app should be checked against the current plan documentation rather than assumed from a demonstration or another user’s screen.

Verification also changes with the task. For prose, compare names, dates, quotations, and links with the original material. For code, run tests and review security-sensitive changes. For calculations, reproduce the result in a spreadsheet or trusted program. Web search can expose supporting links, but the presence of links does not remove the need to read them.

Privacy deserves the same task-specific approach. OpenAI’s Data Controls FAQ explains consumer controls for chat history and model improvement. Business and organizational products can follow different terms and administrator settings. Before adding customer records, unpublished research, source code, or health information, identify the account type and review its controls. Our guide to finding chats and files in ChatGPT provides more context on organizing work inside the product.

2. Claude: document-centered reading and writing

Anthropic describes Claude as an assistant for writing, analysis, coding, research, and working with files. Its task fit is strongest when the job involves reading source material and maintaining a coherent line of thought through a substantial draft. Examples include comparing policy documents, turning meeting material into a structured brief, explaining a codebase, or revising a long report.

That does not make every long answer reliable. A fluent summary can omit an exception, blend two passages, or produce a quotation that needs checking. Ask for references to the supplied material, then inspect the relevant passage yourself. If a decision depends on a table, contract clause, or numerical result, verify that item separately rather than approving the entire answer because most of it reads well.

Claude’s usefulness also depends on the surrounding workflow. Teams may care about projects, integrations, usage limits, account administration, or API access, while an individual may only need the chat product. Those are separate purchasing questions. Check the documentation for the exact product and plan instead of treating all Claude interfaces as interchangeable.

Anthropic’s help page on sensitive data and conversation access explains privacy settings, limited review, and reasons not to enter highly sensitive information into ordinary consumer chats. Organization-managed accounts have their own controls and policies. The safe default is simple: remove unnecessary personal details and confirm the governing terms before uploading confidential files.

3. Gemini: useful when work already lives with Google

Gemini is the most natural candidate in this group for someone whose daily work already revolves around Google services. Google’s product documentation describes Gemini Apps as conversational assistants with capabilities that can include file analysis, image work, and connections to other apps. The practical attraction is continuity: information may already be in the services where the user writes, stores files, manages messages, or plans work.

That connection is also the main reason to slow down and inspect settings. A connected app expands what the assistant can access or act on. Availability varies by device, region, subscription, age, and whether the account is personal, educational, or managed through Google Workspace. Confirm which connection is active and share only the information needed for the current request.

Google explicitly warns in its official page about responses from Gemini Apps that responses can be inaccurate and should be checked. For factual work, open the cited or original page and verify the passage. For generated code, test it. For an email or document summary, compare any deadlines, amounts, and names with the source.

The Gemini Apps Privacy Hub explains activity, human review, retention, and related controls. Those details matter more than a generic claim that a product is private or secure. Review the settings on the account actually being used, especially before connecting workplace data or submitting feedback that includes a conversation.

4. Perplexity: research with citations in view

Perplexity describes itself in its official product explanation as an AI-powered search engine that answers questions with citations and links to original sources. This makes it a sensible starting point for current web research, unfamiliar terminology, product comparisons, or finding material that deserves a closer read.

Its interface reduces the distance between an answer and the pages used to produce it. That is useful, but it is not the same as proof. Open each important citation. Check whether the source is primary, whether it is current, and whether it supports the precise claim. A link may discuss the same topic without establishing the number, cause, or conclusion in the generated answer. For consequential research, search beyond the first response and compare independent sources.

Perplexity is less suited to a task that must stay inside a closed, carefully selected document collection. Web retrieval can introduce pages of mixed quality, and summaries can flatten disagreement between sources. A focused source notebook may be better for a course pack, case file, or internal research bundle.

Privacy settings should be reviewed before searches contain private context. Perplexity’s page on data collection and training controls distinguishes consumer choices from Enterprise handling. Do not assume a personal account has the same terms as an organizational plan.

5. NotebookLM: answers bounded by your source set

NotebookLM takes a different approach from a general chatbot or web search assistant. The user creates a notebook from selected sources, then asks questions and generates material in that context. Google’s help documentation says NotebookLM chat uses citations tied to source passages, allowing a reader to open the quoted context.

This design fits a defined body of material: lecture readings, interview transcripts, project documents, research papers, or a group of reports. It can help locate themes, compare passages, draft study questions, and trace a statement back to the source set. The constraint is often a benefit. It keeps the conversation closer to material the user chose.

The same boundary creates its main limitation. NotebookLM cannot repair a weak collection. If the notebook lacks the newest policy, contains a flawed report, or represents only one side of a dispute, the output may reproduce that gap. Review the source list before trusting the synthesis, follow citations to their context, and add missing or corrective sources when needed.

Google’s NotebookLM privacy and terms page explains how handling differs for consumer, Workspace, and education users, including what happens when feedback is submitted. Account type matters. A notebook containing confidential interviews or internal records should only be created after the owner confirms that the applicable terms and organizational policy permit it.

Source verification checklist for AI-assisted writing and research

How to choose without chasing a universal winner

Start with the material and the required output. Choose ChatGPT when the work moves across many formats and one broad interface is useful. Consider Claude when reading and shaping substantial documents is central. Gemini is a logical candidate when the relevant work already sits in Google services and the connection settings are acceptable. Perplexity fits open-web discovery where citations should remain visible. NotebookLM fits a bounded collection that you want to question and organize.

Then compare the current plans against one real task. Look for the required file types, source behavior, connectors, usage allowances, export options, account administration, and privacy terms. Do not score a product on features the task will never use. A modest tool that keeps the source trail clear may be more appropriate than a broader tool with an impressive feature list.

Whichever product you select, keep verification proportional to risk. A brainstorming list needs a quick sense check. Published facts require source review. Code needs execution and tests. Legal, medical, financial, academic, and employment decisions require qualified human judgment and authoritative records. For writing work, this guide to responsible AI-assisted writing explains why disclosure and source checking matter.

Frequently asked questions

Which of these five AI tools is best for everyday work?

There is no objective winner for every task. ChatGPT is broad, Claude is well suited to document-centered work, Gemini connects closely with Google’s ecosystem, Perplexity emphasizes web citations, and NotebookLM works from a selected source set. Choose based on the material, output, verification needs, and account terms.

Which tool is best for research?

Perplexity is designed for web research with visible citations. NotebookLM is often a better fit when research must stay within sources you provide. ChatGPT, Claude, and Gemini can also support research, but the reader should inspect original sources rather than treating a generated answer as evidence.

Can confidential documents be uploaded to these tools?

Do not upload them by default. First identify whether the account is consumer, business, enterprise, education, or organization-managed. Read the applicable privacy and retention terms, check administrator policy, remove unnecessary personal data, and obtain permission from the document owner when required.

Do citations make an AI answer trustworthy?

Citations make verification easier, but they do not guarantee accuracy. Open the cited page, confirm that it supports the nearby claim, check its date and authority, and look for missing context. For high-stakes work, compare more than one authoritative source.

Related update: AI Enters New Phase: From Instrument to Partner in 2026.

7 Best Mobile AI Apps for iPhone and Android

Muawia Ali
-
0
7 Best Mobile AI Apps for iPhone and Android

Your phone is often where an AI assistant becomes genuinely useful. It is already beside you when a sign needs translating, a meeting note needs cleaning up, or a question appears during a commute. The hard part is not finding an AI app. It is choosing one that fits the job without handing every app access to your photos, microphone, files, contacts, and location.

This guide compares seven of the best mobile AI apps for iPhone and Android by their practical strengths, not by a pretend universal ranking. The shortlist covers general assistance, Google services, careful writing, Microsoft work, source-led search, study from your own material, and visual creation. Features can vary by device, account, region, and plan, so confirm the current app listing and provider documentation before depending on a particular option.

Quick picks: which mobile AI app fits your job?

  • ChatGPT: a flexible general assistant for conversations, files, images, voice, brainstorming, and everyday problem solving.
  • Gemini: a strong fit for people who want an assistant connected to Google services and, on Android, deeper assistant behavior.
  • Claude: a thoughtful conversational workspace for drafting, analysis, and working through complex material.
  • Microsoft 365 Copilot: the most natural candidate when your work already lives in an eligible Microsoft 365 environment.
  • Perplexity: useful when a question should begin with web research and visible sources rather than a blank chat.
  • NotebookLM: designed for learning and asking questions about a collection of sources you choose.
  • Adobe Firefly: the specialist choice here for generating and editing visual media on a phone.

That summary is a starting point, not a verdict. Install the smallest number of apps that covers your real routines. One general assistant plus one specialist tool is usually easier to manage than seven overlapping accounts.

Decision map matching seven mobile AI apps to chat, Google tasks, writing, Microsoft work, web research, source study, and visual creation
Start with the task you repeat most often, then compare permissions and output quality on your own material.

How these seven apps were selected

“Best” can become meaningless when every product is judged by a different demo. This comparison uses four practical filters. First, the provider offers an official mobile experience on both iOS and Android. Second, the app has a clear use case that can matter on a phone. Third, current capabilities can be checked against first-party documentation. Fourth, the group is varied enough to help readers choose rather than simply listing seven similar chatbots.

This is not a laboratory benchmark, and no private test results are implied. AI answers also change with the prompt, model, account, and service updates. A better personal comparison uses the same three harmless tasks in each candidate: summarize a non-sensitive page, explain a topic you understand well, and turn a rough note into a useful format. Judge accuracy, citations when relevant, editing effort, permission requests, and how easy it is to delete the conversation afterward.

1. ChatGPT: the versatile everyday assistant

OpenAI’s current ChatGPT release notes document mobile support across iOS and Android, including voice, search across conversations, files, and other app changes. The exact tools visible in your account can differ by plan or rollout. That broad interface makes ChatGPT a sensible first app for someone who wants one place to brainstorm, explain, rewrite, inspect an image, or talk through a problem.

Its strength is continuity between different kinds of input. You can begin with a typed question, add a photo or supported file when available, and refine the result through conversation. On a phone, that feels more natural than moving material between separate utilities. It is especially handy for turning an unstructured note into a checklist, generating questions before an appointment, or explaining unfamiliar language in a document.

Use it with clear boundaries. Do not upload confidential work, health records, identification, or private conversations unless you are authorized and have checked the applicable data controls. Important facts still need verification against primary material. For a broader workflow, see our practical ChatGPT how-to guide.

2. Gemini: the Google-centered mobile assistant

Gemini is the clearest fit for people whose mobile day already revolves around Google. Google’s official Gemini mobile app guide says the app supports text, voice, images, and camera input. It also explains that Gemini can work with connected apps such as Google Workspace and YouTube Music, while warning that availability varies by language, device, account, and location.

Android users may value Gemini as a mobile assistant, while iPhone users get the dedicated Gemini app. Either way, connections deserve attention. A connection that saves time can also expand the information available to the service. Enable only the connected apps needed for a current workflow, and review location, camera, microphone, and photo permissions at the operating system level.

Gemini is a practical candidate for planning with Google context, discussing something seen through the camera, or having a live spoken exchange. It is less compelling if you do not use Google services or if your managed work account blocks the features you need. Check the account type before building a routine around it.

3. Claude: a focused space for writing and analysis

Anthropic’s official getting started guide confirms Claude access through iOS and Android apps. The same guide recommends natural, conversational prompts and describes using Claude for questions, analysis, creative work, coding, and multi-step projects. For mobile users, Claude works well as a relatively uncluttered place to develop an idea through several thoughtful revisions.

Consider Claude when the desired result is not a one-line answer but a clearer argument, an outline, a critique, or a careful transformation of supplied text. A good mobile prompt includes the audience, purpose, constraints, and the form of the answer. Then ask it to identify uncertainty rather than silently smoothing over gaps.

Do not confuse polished prose with verified truth. If Claude summarizes a source, compare the summary with the source. If it proposes a technical or legal interpretation, consult authoritative guidance. Availability is also limited to Anthropic’s supported locations, and usage limits depend on the service and plan.

4. Microsoft 365 Copilot: mobile AI for Microsoft work

Microsoft’s official mobile guide describes Microsoft 365 Copilot for both iOS and Android. It highlights chat, voice, search, Library, Create, Agents, and Notebooks, while making an important qualification: what appears depends on the Microsoft license attached to the account.

That qualification is central to choosing it. Copilot is most useful when an organization has approved Microsoft 365 workflows and the user has legitimate access to the relevant files, messages, or meetings. In that context, a phone can be a convenient place to catch up, ask about permitted work content, or draft a response before returning to a larger screen.

Personal users and managed work users should not assume they receive the same grounding or tools. Check whether you are signed into the intended account, confirm what information the prompt can access, and review generated text before sending it. A concise summary on a small screen can hide a missing exception, name, or number.

5. Perplexity: a research-first answer engine

Perplexity is a useful mobile choice when the first question is “What do current sources say?” Its official mobile help collection documents iOS and Android assistant experiences, including voice options and Android-specific tools. The product’s research orientation can make it quicker to move from a question to pages worth opening.

The citations are the beginning of verification, not the end. Open the cited page, confirm that it supports the nearby sentence, look at publication context, and prefer primary sources. A citation can be real yet irrelevant, outdated, or too weak for the conclusion. For consequential decisions, build your own short source list instead of relying on the generated synthesis alone.

Perplexity suits news discovery, product research, travel questions, and quick orientation when the web matters. It is not automatically the right tool for private documents or a long creative draft. Keep the distinction between web discovery and confidential work clear.

6. NotebookLM: study grounded in sources you choose

NotebookLM takes a different approach from a general chatbot. You create a notebook around selected sources, then use the tool to explore that material. Google’s official mobile app announcement confirms iOS and Android apps and describes asking questions about sources, sharing content to NotebookLM while browsing, and listening to Audio Overviews on the go.

That source-centered design is valuable for a reading packet, course material, product documentation, interview transcripts, or a collection of reports. The quality of the notebook still depends on what you add. A neat answer grounded in a poor or one-sided source set remains poor or one-sided. Label sources clearly, include competing viewpoints where appropriate, and trace important statements back to the underlying page.

NotebookLM is the specialist pick for understanding a bounded collection. It is not the first choice for an open-ended request that needs broad web discovery. Also check whether you have permission to upload each document. “For study” does not cancel copyright, workplace, or privacy obligations.

7. Adobe Firefly: AI creation for visual work

Most apps in this list lead with words. Adobe Firefly earns a place because some mobile jobs are visual. Adobe’s official Firefly product page states that the app is available on iOS and Android and lists image, video, sound effect, and generative expansion tools. It also describes Creative Cloud syncing, which can help a creator start an idea on a phone and continue elsewhere.

Firefly is the focused option for concept images, visual variations, and edits rather than a substitute for a general research assistant. Start with a clear creative brief that names the subject, composition, mood, and output purpose. Then inspect hands, text, logos, faces, and background details carefully. Generated media can contain visual errors even when the first glance looks convincing.

Before publishing, check ownership, brand, consent, and disclosure requirements for the destination. Do not assume that an available generation tool makes every requested use appropriate. For client or workplace material, follow the organization’s approved asset and review process.

A practical comparison by task

Primary need Good first candidate What to verify
Mixed everyday assistance ChatGPT Available tools, data controls, and factual accuracy
Google-connected help Gemini Connected apps, account eligibility, and location permissions
Drafting and sustained analysis Claude Source fidelity, supported location, and usage limits
Microsoft work context Microsoft 365 Copilot Correct account, license, and organizational approval
Current web research Perplexity Whether each citation truly supports the answer
Learning from selected sources NotebookLM Source quality and permission to upload
Visual generation and editing Adobe Firefly Visual defects, rights, brand rules, and disclosure
Five step privacy and accuracy check for mobile AI apps covering task, data, permissions, verification, and cleanup
A short preflight prevents a convenient mobile prompt from becoming an unnecessary privacy or accuracy problem.

Use this five-step mobile AI preflight

  1. Name the task. Decide whether you need open web research, help with your own sources, workplace context, conversation, or visual creation. This prevents choosing by brand recognition alone.
  2. Reduce the data. Remove names, account numbers, private images, internal details, and anything else the app does not need. If the task can be completed with a fictional example, use one.
  3. Check permissions and connections. Review camera, microphone, photos, location, contacts, notifications, and connected services. “Allow only while using” or selected-photo access may be enough for many tasks.
  4. Verify the output. Open cited sources, check numbers and names, inspect generated images, and compare summaries with the original material. Never let a fluent answer bypass professional review.
  5. Clean up. Save the useful result in the correct system, then delete temporary chats or uploads when policy and app controls permit. Revoke connections you enabled only for a one-time job.

Readers who want a deeper privacy setup can use our guide to safer ChatGPT privacy settings and data controls. The exact menus differ across providers, but the habit of minimizing access applies to every app in this list.

How to run a fair three-day comparison

Choose no more than three candidates. Write down three ordinary tasks that contain no sensitive information. For example, ask each app to explain a familiar concept for a beginner, summarize the same public source, and convert the same messy note into a checklist. Use equivalent prompts and record how much correction each answer needs.

On day two, test the mobile-specific interaction you care about, such as voice, camera input, source sharing, or a work account. Do not enable every permission at once. Grant the minimum access needed, observe whether the app explains why it needs that access, and turn it off afterward if the workflow does not justify it.

On day three, review the less exciting details: deletion controls, account switching, export options, notification settings, battery use, accessibility, and whether the app still helps on an unreliable connection. Then keep the candidate that reduces work without creating disproportionate review or privacy overhead. Remove the others, along with permissions and connected accounts they no longer need.

Frequently Asked Questions

What is the best free AI app for iPhone and Android?

There is no single winner for everyone, and free access, tools, and limits can change. Start with the app that matches your main task, then confirm its current listing and account limits. ChatGPT or Gemini may suit broad assistance, Perplexity may suit web discovery, and NotebookLM may suit study from selected sources. Do not choose on the word “free” alone. Consider permissions, correction time, and whether a required feature is actually available to your account.

Do I need more than one mobile AI app?

Often, no. A general assistant can cover everyday questions, drafting, and brainstorming. Add a second specialist only when you repeatedly need something distinct, such as source-grounded notebooks or visual generation. Fewer apps mean fewer accounts, permissions, notifications, and privacy settings to maintain.

Are mobile AI apps safe for confidential work?

Do not assume they are. Safety depends on the provider, account type, organizational agreement, settings, and the data involved. Follow your employer’s approved-tool policy, minimize what you submit, and avoid confidential material in a personal account. Check the current provider documentation and your organization’s requirements before using any AI service with protected information.

How can I tell whether an AI answer is accurate?

Ask for sources when the task supports them, but open and read those sources yourself. Compare summaries with the original text, verify names and numbers, and use an independent primary source for consequential claims. If you already know the topic, test the app with a question that includes a subtle trap. The goal is not to make the model agree with you. It is to learn how much checking its output requires.

The bottom line

The best mobile AI app is the one that handles a repeated task well, asks for proportionate access, and produces output you can realistically verify. ChatGPT is the broad all-rounder in this group. Gemini fits Google-centered routines. Claude is a strong writing and analysis space. Microsoft 365 Copilot belongs in approved Microsoft workflows. Perplexity starts with web research. NotebookLM starts with your chosen sources. Adobe Firefly specializes in visual creation.

Pick by workflow, not hype. Test with harmless material, review permissions, keep primary sources close, and uninstall tools that do not earn a place on your home screen.

123...14Page 2 of 14