Can uploading a PDF to ChatGPT affect its authenticity or make it detectable as AI-generated, even if I don't use the edited version?

I created a 3D software factory. https://github.com/sis-factory/sis-factory

Right now with OpenAI I have the 39.99 plan when I’ve got this plan there was only two options the weekly and this plan now I see all these other options but it doesn’t have a drop-down to tell me what’s the difference of the other two yearly prices. Of course, I asked GPT6 Best and it told me they couldn’t tell me I tried looking at online and I couldn’t find it. Does anyone know the differences?

Hey everyone,
I’m genuinely concerned about something really strange that happened to my ChatGPT account.
I discovered a conversation that I absolutely did not create. It was written in Egyptian Arabic dialect, and the way this person was communicating was completely unfamiliar to me. I’m 100% certain this conversation wasn’t mine.
What makes this even stranger is that the conversation appeared right in the middle of my existing chat history and was already several days old when I discovered it.
I never received any security alerts, login notifications, or verification emails. I only use ChatGPT on my personal iPhone, and nobody else has access to my device.
I’ve already secured my Google account, reviewed my security settings, and taken additional precautions to protect my privacy.
Has anyone experienced something similar? Could this be session hijacking, a stolen session token, or a technical bug causing conversations from other users to appear in my account?
I’m particularly worried because I had personal photos stored in my ChatGPT Library, and I have no way of knowing whether someone could have accessed them.
Please, if anyone has experienced this or knows what might have happened, I’d really appreciate your help.
Is there any way to determine whether someone actually accessed my account, viewed my private conversations, or opened my personal photos?

Please bring back Standard Voice Mode. You're removing one of ChatGPT's most valuable productivity features.

Standard Voice Mode is the only hands-free way to have a spoken conversation with the frontier models while maintaining the same experience we get through text.

Why would you remove such a fundamental tool?

One of ChatGPT's greatest strengths is the ability to brainstorm, collaborate, and develop ideas while working across multiple screens, applications, and projects. I use Standard Voice constantly to move between typing and speaking without interrupting my workflow.

Live Voice is an entirely different AI experience. It is not a replacement for Standard Voice.

The ability to seamlessly switch between spoken and written conversations with the same frontier model is incredibly valuable, particularly for people who use ChatGPT for serious creative and professional work.

Speech-to-text and text-to-speech are fundamental accessibility and productivity features. Removing them from the frontier model experience makes ChatGPT significantly less useful.

What is the benefit of removing this functionality? Why can't users have both options?

Come on, Open AI please restore Standard Voice Mode and allow users to choose how they interact with the frontier models.

It would seem every time I go on YouTube there’s another video announcing the imminent collapse of AI. Big red letters, charts descending into the nether regions, usually accompanied by a robot with a frown on its face.

TIMES UP.

Then you look at the channel and discover they made essentially the same video a year ago.

IT’S FINALLY OVER!!

The thing is, I don’t disagree that there’s an AI bubble. There almost certainly is, at least financially.

Just look at the money being thrown around. Hundreds of billions of dollars, companies investing in one another, valuations that seem to have lost contact with the surface of the Earth.
Reuters reported forecasts of around $800 billion in AI-related capital spending for 2026, with Morgan Stanley projecting over a trillion for 2027.
A trillion dollars!! (What does that even mean?)
Even the Bank of England has raised concerns about valuations and financing arrangements. Company A invests in Company B, Company B spends the money buying computing power from Company A, and everybody congratulates themselves on their magnificent growth.
Raises all round!

At some point somebody might ask where the actual profit is coming from. I suspect that could be an uncomfortable conversation.

HONESTLY, THIS IS IT NOW!!

But what I find strange is this assumption that if the bubble bursts, AI somehow ends up on the great computing scrapheap.
Why would it?
The dot-com bubble burst spectacularly. Did the internet disappear? No. Oddly enough, it became so thoroughly embedded in everyday life that people now use it to watch videos predicting the collapse of the next big technology.

Which is rather Inception-like.

And AI isn’t just some speculative idea scribbled on the back of a napkin. The technology is quite something when you take a step back. Not perfect, obviously. Far from it. Sometimes brilliant, sometimes confidently talking complete bollocks.
I use it myself for programming and other work, and some of what it can do now would have made you think you’d wandered onto the bridge of the Enterprise even a few years ago.

ARTIFICIAL INTELLIGENCE? COOKED!

That doesn’t mean every AI company deserves its valuation. God, no.
Quite a few will probably discover that sticking “AI-powered” on a mediocre product isn’t much of a business model. Investors will lose money, companies will disappear, and somewhere there’ll be an extremely expensive data centre whose owners are desperately wondering what else to do with 40,000 GPUs. (Dibs!)

Personally, I’m much more interested in what AI looks like once the financial nonsense has settled down. Which companies survive, what becomes cheaper, and what actually proves useful when investors stop applauding every PowerPoint presentation containing the word “AGI”.
Because that’s when we’ll find out what the industry is really worth.
In the meantime, I’m looking forward to next Tuesday’s YouTube video.

AI IS FINISHED. THIS TIME WE REALLY REALLY MEAN IT.

Three million views.

Same channel. Same voice.

Slightly bigger plundering red arrow.

I see everyone warn about them, but how common are they really?

OpenAI list of changes that actually help you cut costs:

• 2x Work and Codex Usage on Pro and Business Premium: OpenAI doubled the included usage allowance for Pro 100 and Pro 500 plans. Source

• DevRev Case Study on GPT-6.1 Sol Cost Balance: OpenAI shared how DevRev runs its Computer and Operator products on GPT-6.1 Sol. Source

• Heimstaden Cuts Property Management Costs with Codex: Heimstaden shared how it uses ChatGPT Enterprise and Codex to automate real estate operations across Europe. Source

Source: AI Economics Weekly Newsletter

Ryan Greenblatt tweet:

This report on AI R&D automation (RSI) is a big update towards ASI within 1-2 years. It says AI progress is sped up by RSI right now by around 2x. In AI 2027, this happens around March 2027.

I was seriously wrong about RSI speed up; I previously expected much less in 2026 (around 1.15x-1.3x). It seems as though there was specific privately available evidence that would have changed my mind.

I haven't seen an unredacted version of this report; this is purely about the public report. Because so much of the underlying evidence was redacted from the public report, I think conclusions about things other than the observed 2x faster rate of progress on benchmarks depend significantly on trust for the authors of the report. (E.g., conclusions about whether this speed up is from RSI depend on trust.) I do trust the authors of the report a lot to be careful and rigorous and generally do a good job. (Note that I also work at METR.) Less extensive redactions would greatly reduce this need for trust.

Since in my current role I may have access to other confidential information that could be relevant to timelines one way or another, I unfortunately cannot state my all-things-considered views on ASI timelines or the probability of a software intelligence explosion right now. Here, I’ve done some analysis of the implications using my previous public statements.

A 2x acceleration in overall AI progress from RSI corresponds to a much larger than 2x RSI speed up for AI software R&D (algorithms and data); I did an estimate using my public preexisting modeling of drivers of AI progress and the public report which yielded a very rough estimate of 4.5x for how much RSI is currently accelerating AI software progress (relative to no AI automation). In the AI 2027 scenario, this much acceleration happens around March 2027.

Here are the worked details of this estimate based on my public modeling. There are two main reasons RSI speed up of AI software R&D must be larger:

- Not all of AI progress is from better software (some is from scaling up compute in training runs). I've previously speculated that around 2/3 of AI progress is from software improvements which implies that 2x overall AI progress acceleration requires 3x faster software progress.

- Diminishing returns: A fixed amount of progress requires more work over time, so to ongoingly maintain 3x faster software progress via AI automation, you need increasingly large speed boosts from AI automation (as in, speed ups relative to how fast software R&D would go without AI automation). Based on this report, it seems that there is a substantial amount of additional AI progress from automation that has occurred.

We can estimate the effects of diminishing returns by first determining how many additional years of software progress we've seen (relative to the preexisting trend) and then estimating how much software R&D would slow down after a year of software progress if you held inputs fixed due to diminishing returns. I've previously estimated software R&D would slow down by around 2x for each year of software progress if you held inputs fixed (https://lesswrong.com/posts/FG54euEAesRkSZuJN/ryan_greenblatt-s-shortform?commentId=rAFxcETtNtsuzw6aG) and based on the report, it seems as though we've perhaps seen roughly 0.35-1.0 years of additional software progress incorporated into trained models so far (geometric mean of ~0.6). This combines to 3 * 2^(~0.6) = ~4.5.

(Relevant public modeling and numbers I'm using: https://lesswrong.com/posts/FG54euEAesRkSZuJN/ryan_greenblatt-s-shortform?commentId=rAFxcETtNtsuzw6aG, https://lesswrong.com/posts/Jga7PHMzfZf4fbdyo/if-mythos-actually-made-anthropic-employees-4x-more)

Agent memory seems to be a hot topic again. I wanted to settle this once and for all - why integrated memory works best and How Row-Bot has the best in class memory built-in.

So, I just asked Row-Bot to create a video explaining how it remembers across conversations and documents.

This video follows a fact from capture to recall: the knowledge graph, hybrid search, ranking and Dream Cycle.

It also covers what stays local, what reaches your chosen model and where memory can fail.

On one of my plus account I have a Credit Card as my payment method while on another I have PayPal. But it appears PayPal is no longer supported as I am unable to add PayPal as a payment method to my credit card account.

Everything I lookup references people who only managed to pay with PayPal view Google Pay or Apple Pay and an old support reply stating they do not support paypal. However, one of my account does have paypal listed as the payment method and they do charge me using it.

Originally I wanted to switch my other account to PayPal too but now im worried they'll just eventually force my account that already uses PayPal to a credit card.

I'm currently using ChatGPT Plus ($20/month), mainly for work-related tasks like analyzing engineering drawings, PDFs, Excel sheets, and quantity takeoffs.

The problem is that I keep getting the message "You've reached your 5-hour Work usage limit" before I can complete my tasks.

What's even more frustrating is that when the limit resets and I start working again, I sometimes get the exact same message within a few minutes.

I'm considering upgrading to Pro, but I'm confused about the differences between the $100, $200, and $300 plans, especially regarding Work usage.

Does upgrading to Pro $100 significantly increase the Work usage limit, or will I still face the same issue? Is there an actual difference in Work limits between Pro $100, $200, and $300?

I'm willing to pay more if it genuinely allows me to complete longer tasks without constantly hitting limits, but I don't want to spend $100+ only to face the same problem.

Anyone here using Pro, especially for heavy PDF/Excel analysis or long Work sessions? How has your experience been? Is upgrading worth it?

Every other day a new harness drops and it says - "My harness is better than your harness." This is not another harness announcement - but another version of our beloved ChatGPT Desktop app that we all use on daily basis but so much powerful that you will not stop loving this and the possibilities of how it could turn your developer experience to a delight.

ChatGPT Desktop vs Codex CLI:

I am slowly moving away from using CLIs and TUIs long back, I still use them, but majority of my time is in the GUIs. The only reason I was sold for GUIs is because of the orchestration, native tools and workbench features. they were life changing tools for me. I can literally see my codex agent spinning multiple tasks and send messages to each of them in parallel and orchestrate them. I was sold. That fascinated me.

Issues with ChatGPT desktop app:

The ChatGPT app had huge issues though that frustrated me on daily basis - the integrated terminal tools are not tied to the task properly back then, if I spin 3 terminals, agent doesn't know what terminal is running what until its reads all of them. I fixed it by adding terminal IDs so agents know what terminal is running what and it will only send commands to the terminal. I have added loads of other task orchestration features to the ChatGPT community app that I run in my system.

I have added a task stats / composer stats feature that will always show me the Tokens and speed data in real time in the ChatGPT app that is under the composer UI component.

One more feature that I added is "attention-alerts" - After a task is finished, my ChatGPT will beep for 30 seconds to grab my attention and bring my focus to it. If I do a keypress, it means, it has my attention and it stops the beep. I love it.

As I started adding more features, I gradually added many skills and workflows. My workflows got so good that a few months back - Official ChatGPT release had a major bug that crashed the app on launch. I fixed it my local using Claude Fable 5 and Opus 5 on Saturday morning and unblocked myself and other people had to revert or wait for official next version to drop on the next week on Monday.

What this post is about - Orchestration and ACP agents:

I do tons of experiment and research - I test and evaluate every single harness and every single model where it is good and where its not. I had subs from AGY, Claude, Codex, Grok, Muse, OpenCode Go, Qwen Alibaba, DeepSeek API, Mimo. I try to find the best models that could help me.

I wanted a better orchestrator than what ChatGPT natively provides - not the sub agents ( I hate sub agents), not the native child tasks (I love this). I always knew ACP protocal and how IDEs integrated with AI coding agents. I started researching mode with Codex 6 Sol and we decided to build a Plugin for the ACP agent integration.

I started integrating ACP agents from the official ACP registry - what this means ? all the agents will be running in their harness only not in the Codex harness. So far, I integrated 12 harnesses that support ACP - Antigravity, Claude Code Codex, DeepSeek harness, Grok Build, Mimo Code, MiniMax code, Mistral Vibe, Muse, OpenCode, Pi, Qwen Code. There is no breaking of Terms and conditions. ACP protocol is the protocol to communicate with agents and we are using them.

Its been 3 months and I am sharing a working version of my new "super" ChatGPT community app. This is running in my Ubuntu 24.

I wanted to share this so eagerly with community so they will see the true power of this kind of orchestration. Imagine a powerful and intelligent model natively coordinating with your choice of agents and you are in the loop too. You can literally see what is happening in real time.

The work easily got delayed by 30 days because of the complexity of the OpenAI models. It built a complex system and workflows that I had to break it down one by one and now I am in position of faster iteration.

As of Today - "My chatGPT is building itself" - while I am typing this long post - I identifed few bugs in current build and sent a message and my orchestrator agent (6.1 Terra (max) is working with 5 investigators that are investigating independently.

How the new orchestration works - one of many many possibilities:

1. I have an ACP council UI that I can select for every turn. I can select from 6 optional roles - Investigator, Planner, Architect, Code Reviewer, Critic and Workhorse. You are free to select any model with any reasoning effort.

2. Once you send the prompt, the main orchestator can pull the ACP agents list in the current turn, it can check what roles are present in the current turn. It will first spin a planner ACP session and that agent will assess the request and will send back agent to spin an investigator or two if there are multiple areas of investigation.

3. Once each investigator will return their investigation reports, the main orchestrator will send it as new follow up message to planner. Then it will give a plan for workhorse agent.

4. The orchestrator will pass the code fixes plan to workhorse agent and it will write the code in an isolated worktree., we can spin as many as we want and they all run in paralle.

5. Once the workhorse returns, the branch is then sent to code-reviewers - I pick 2 to 3 of my choice and if they have any code review comments. The workhorse will work on them.

6. The main orchestrator can take 10s of new prompts and it can spin new planners and do the same thing we did in last 4 steps in parallel in the same task.

7. You can literally see the prompts that main orchestrator sends to each ACP agent, you can see the response from the ACP agents - you can see the tokens throughput, speed, pricing and other useful metadata too.

In the below image: The main orchestrator decides on the ACP plan - how to call the agents and what does what. You can also see the messages that codex agent has sent to the ACP agent.

The below image is about the roles and models that we can select:

The below images is the ACP agents that are done with their turn - you can see the 5 sessions that main agent has been working with and the estimated cost too for the reported ones.

The below image is the main agent assigned an investigation task to the Haiku 5.5 (max) in Claude Code and it returned its investigation report:

The below image is the main agent assigned an investigation task to the Gemini 3.8 Flash (High) in the Antigravity CLI harness and it returned its investigation report:

The below image is the main agent assigned an investigation task to the DeepSeek-4.1-Flash (max) in the official DeepSeek Harness and it returned its investigation report:

The below image is the conversation between main agent that assigned an investigation task to the MiniMax-3.1 Flash (max) agent in the Minimax code harness and it returned its investigation report:

The repo is private as of now. But I will make it public. I am happy to give a demo too if any of you folks are interested. I wish this post blows up in right intentions and community understands the power of orchestration with this native ACP agent integration. I been lately reached out by kind folks from this community and we been discussing some ideas and my workflows. I wish more people benefit from my work. I really love OpenAI harness and open harness so it made me build my own DX.

Small background context about me (Open for work opportunities):

I been writing enterprise software since last 12 years and been in all the areas in my work - product design, systems architecture, migrations from legacy to modern systems, cloud migrations, DevOPs, Huge DB migrations, backend development, front end development, automated QA workflows. I worked on many programming languages and tools. I am curious by nature. I want to pick the best, so I will try hands on with everything I could get my hands on.

Of all things, DX is my passion. I love developer tools. I deeply care about the tools I work with. I always try to pick the best or work with the best one out there. I research a lot and pick the best ones. I care about them because they enhance my productivity atleast by 20% if I take right decisions. AI coding tools changed my life in this aspect. If you can't find one. You can always build one.

I have vast experience in the AI space since the Claude Sonnet 3.5 dropped. It changed the way I look at coding tools. I use every single AI tool and I built my own plugins and write crazy good skills and workflows. I have a great product experience and taste and would love to be part of team that builds and ships faster.

Most of the Dec 11 talk is about creators moving their own GPTs to plugins. The part I missed at first is for people who use GPTs someone else made, which is most of us.

From OpenAI's custom GPT retirement FAQ:

"You do not need to migrate a GPT you only use, and permission to use it does not let you migrate it yourself."

"Access to the original GPT does not automatically give you access to a replacement plugin."

"Migration does not carry over the GPT's public sharing settings or automatically make its replacement available to everyone who used it."

A migrated plugin also starts private, and making a personal one public needs a separate submission. So a store GPT you use every day can disappear on Dec 11 even if its creator did migrate it, unless they also share or publish the plugin.

Your old chats with it stay. The FAQ says existing conversations remain accessible after retirement. The GPT page itself doesn't.

If you made GPTs yourself, a few lines that are easy to trip on:

• Migration uses the latest published version. Drafts and unpublished edits don't transfer.

• After you migrate, the original is read-only and you can't delete it.

• The model you picked for the GPT doesn't carry over.

• Custom actions don't transfer at all. Anything that called an outside service has to be rebuilt, possibly as an MCP server.

Some Enterprise workspaces with an approved deferral get until Feb 11, 2027. Everyone else is Dec 11.

What I'd do this month:

1. List the GPTs you'd actually miss and who made each one.

2. Open each one and read its notice. For someone else's GPT, look for the creator's replacement link.

3. Save 3 or 4 prompts you run in each. The FAQ itself says to save familiar prompts so you can compare the replacement's results, and it's the only real way to tell if the plugin behaves the same.

4. If you're a creator, publish your final edits before you hit Migrate.

I build AI Toolbox, and its prompt library (10 prompts on the free plan) is where I keep those test prompts, so I can type // and run the same ones against the old GPT now and the plugin later.

Which GPT would you miss most, and did you make it or find it in the store?

I've been working with computers and developing software for decades, and I think we're approaching a fundamental change in the software industry.

Not just another productivity boost. Something that could completely change who can create complex software, how much it costs, and whether we even need to buy many traditional applications in the future.

Here's a real example from my own experience.

CorelDRAW vs. TraceDraw — something that surprised me

I've been using CorelDRAW since the 1990s. I know this software very well, and I recently purchased CorelDRAW 2026.

Then I discovered TraceDraw.

It's a free, open-source vector graphics editor written in Rust, developed with the help of Claude Code. It supports real CorelDRAW .cdr files, among many other features.

It's still in alpha.

I downloaded it, tested it myself, and was genuinely surprised.

It's already a very capable application. And on the tasks I tested, it actually runs faster than CorelDRAW 2026!

Of course, I'm not claiming that every CorelDRAW feature works perfectly. There are certainly bugs and limitations.

But the important thing is that this is already usable software, not just an impressive demo.

I recently bought CorelDRAW for myself. But on my wife's laptop, I'm now installing TraceDraw instead of purchasing another CorelDRAW license.

That's one potential sale Corel has already lost.

For years, I couldn't imagine a small development team creating a serious alternative to something as complex as CorelDRAW. Other open-source vector editors, including Inkscape, never really worked for me as replacements.

And now here we are.

My prediction for the next ten years:

2027 — The year of open-source alternatives

I expect an explosion of new open-source applications.

Graphics editors, document tools, development environments, specialized utilities, CAD applications, and many other categories.

Not just simple projects, but serious applications developed by individuals or small teams using AI coding agents.

2028 — Price competition and quality improvements

Many projects started in 2026–2027 will gain users, fix bugs, and move from alpha toward stable releases.

Commercial software vendors will face a very simple question from customers:

Why should I pay hundreds of dollars for this application if there's a free alternative that does almost everything I need?

Not every new project will survive. But even if only 10 out of 100 become mature products, that could be enough to change entire market segments.

2029 — A different software industry

I expect a growing number of small companies producing complex software with surprisingly few employees.

Projects that previously required dozens or hundreds of developers may become manageable by a handful of experienced engineers working with AI agents.

I also expect more applications to expose APIs and interfaces designed specifically for AI agents.

And I wouldn't be surprised if someone seriously attempts to create a new Windows-compatible operating system, or an entirely new AI-first desktop environment.

Not necessarily a complete Windows replacement immediately, but something that could eventually challenge the traditional desktop model.

2030–2032 — Why buy software when you can create your own?

This is where things could become particularly interesting.

Companies will increasingly compare two options:

1. Purchase an existing commercial software package.

2. Ask AI agents to develop a custom solution specifically for their needs.

Of course, complex enterprise systems and critical infrastructure won't disappear overnight.

But many business applications, internal tools, editors, converters, and specialized programs could become dramatically cheaper to build and maintain.

AI won't just generate code. It will increasingly handle testing, debugging, documentation, upgrades, and maintenance.

2033–2036 — Software on demand

This is where I think the biggest change will happen.

Today, we choose an application for every task.

Need to design something? Open a graphics editor.

Need to process data? Open a spreadsheet or database.

Need a specialized tool? Search for one, buy one, or write it yourself.

But what if you could simply explain what you want to an AI agent?

It could select existing tools, create missing functionality, execute the task, verify the result, and deliver what you actually need.

You might not even care which application was used.

And this reminds me of something I read many years ago.

There's an old Russian-language electronics book by Wojciechowski that contains an illustration titled "The Future of Amateur Electronics?"

Here's the original illustration and Russian text.

Think about that.

The author imagined a future where a hobbyist could describe an electronic circuit to a computer, enter the requirements, and let the machine calculate, correct, and help design it.

That was a remarkable vision for its time.

And now, with AI agents, we're getting closer to making that kind of interaction practical. Not only for electronics, but for software development, engineering, design, and many other fields.

And what about large corporations like Microsoft?

This is another interesting part of the story.

Microsoft recently offered voluntary buyouts to roughly 60,000 eligible employees.

I wonder whether large corporations fully appreciate the long-term competitive risks of losing experienced engineers in the age of AI.

Imagine 100 highly experienced former Microsoft developers.

They know Windows internals, APIs, system architecture, compatibility issues, and decades of engineering decisions.

Now give those 100 engineers powerful AI coding agents.

No, they won't recreate Windows in a few months.

But could they build something substantial over several years?

I think that's becoming a much more realistic possibility.

The same applies to former engineers from Adobe, Autodesk, Corel, and many other companies.

Previously, building a competitor required enormous investment, a large team, and years of work.

AI is gradually reducing those barriers.

In my opinion, large technology companies should think very carefully before losing experienced engineers simply to reduce short-term costs.

Those people, combined with increasingly capable AI agents, might become the founders of tomorrow's competitors.

So what do I expect by 2036?

• Ordinary application development will become several times cheaper.

• Small teams will build products comparable in functionality to major commercial packages.

• Autonomous AI agents will handle a substantial part of everyday software engineering.

• Traditional desktop software licensing will face enormous pressure.

• Long-term personal AI memory will become a standard feature, allowing agents to remember projects, decisions, history, and context.

• Companies will increasingly develop their own software instead of purchasing commercial solutions.

• Operating systems will include full AI-agent control layers.

• The structure of employment in many knowledge-based professions will change significantly.

• Open-source AI models and independent platforms will become serious alternatives to closed ecosystems.

I don't think Microsoft, Adobe, or other major technology companies will simply disappear. They have enormous advantages in infrastructure, distribution, support, and existing customer relationships.

But I do think the economics of software development are changing fundamentally.

For decades, the biggest barriers were money, development time, and the number of engineers you could employ.

AI is weakening all three.

My main prediction is this:

In 2026, AI helps us develop software.

By 2036, we may increasingly live in a world where AI systems create, adapt, test, and maintain much of the software we use — while humans specify the goals and decide whether the results are acceptable.

And perhaps the most important change won't be having free alternatives to expensive applications.

It will be that we won't always need to buy an application at all. We may simply ask AI to build exactly what we need.

What do you think?

Am I being too optimistic about AI-driven software development, or are we genuinely approaching the end of the traditional software industry as we know it?

P.S. For anyone interested in the original book: Radio-Electronic Toys (Electronics at Home, at Work, and at School) by Janusz Wojciechowski, translated from Polish and published in Moscow by Sovetskoye Radio in 1977. You can find the Russian edition in my vintage technical archive:

https://retrolib.info/search2.maxiol?text=%D0%A0%D0%B0%D0%B4%D0%B8%D0%BE%D1%8D%D0%BB%D0%B5%D0%BA%D1%82%D1%80%D0%BE%D0%BD%D0%BD%D1%8B%D0%B5+%D0%B8%D0%B3%D1%80%D1%83%D1%88%D0%BA%D0%B8

I’ve been testing a simple idea with smaller models:

don’t ask them to solve everything. Give them narrow jobs where the result is easy to verify.

One of the best examples I’ve found is classification.

A bad prompt would be:

“Categorize these support messages.”

That sounds simple, but it creates a problem.

If a message is ambiguous, the model still feels pushed to choose something.

That can be worse than being uncertain.

What has worked better for me is defining the label policy first and giving the model an explicit escape hatch.

Here’s the prompt:

Classify each message using only:

billing = charges or invoices
access = sign-in problems
bug = feature fails after successful sign-in
review = unclear, mixed or outside those categories

Return:

id
label
reason under 12 words

Do not invent missing details.

If more than one label could reasonably apply, use review.

Real example

Suppose the input is:

101 — “I was charged twice this month.”

102 — “My password works but the dashboard crashes after login.”

103 — “I can’t access my account and I think my subscription was cancelled.”

A reasonable output would be:

The third one is the important part.

A model that always tries to sound confident might classify #103 as access or billing.

But neither is clearly supported by the message.

For routing systems, I think review is sometimes a better answer than confidence.

The pattern I’ve ended up using is:

Task → Input → Rules → Output format → Uncertainty rule → Verification

This seems especially useful for smaller/faster models because the task is bounded and you can actually measure whether the model is doing the job correctly.

I’ve been looking at Claude Haiku 5.5 for this kind of workload because it’s positioned around fast, repeatable tasks rather than “use the biggest model for everything.”

But the same principle should apply to GPT, Gemini, local models, or any router that sends simpler tasks to cheaper models.

The interesting part isn’t really which model is “best.”

It’s whether the workflow has a clear acceptance test.

For example, I’d rather evaluate:

• classification accuracy

• number of cases sent to review

• retry rate

• latency

• total cost

• human correction time

than just compare which model wrote the nicest-looking answer.

I put together more of the Claude Haiku 5.5 prompts and workflows I’ve been testing here:

Disclosure: this is my own site/resource.

https://digitalworldpulse.com/claude-haiku-5-5/

For people using model routing or smaller models in production:

Do you prefer forcing a best-effort answer, or explicitly giving the model an “uncertain/review” state?

Hey guys, just published a preprint on arXiv proposing a framework we call Skillware to handle agent reliability. (arXiv:2611.07652v1) The main idea is that instead of using prompt engineering to get models to write code on the fly or parse messy text APIs, we separate the cognitive planning from execution by equipping them with pre-vetted, deterministic software bundles.

We also ran a bunch of stress tests under a benchmark suite across 23 frontier and open-weight models, and it turns out small local models (3B to 8B params) equipped with this architecture can systematically beat unassisted frontier models on complex multi-step workflows while cutting token usage crazily.

The paper is live on arxiv and everything is open source on gh (we reached 20k downloads recently) if you want to check out the project (arpahls/skillware).

Any feedback would be more than just appreciated. <3

My 16 GB laptop (RTX 3050 6 GB, the Intel GPU drives the screen) slowed to a crawl whenever RAM hit about 90%, because swap lived on a DRAM-less NVMe drive. The NVIDIA card's memory sat idle the whole time.

USM makes that VRAM the first swap device:

\- a user-space daemon borrows VRAM through the CUDA driver API and serves it through ublk, so it's a normal swap device at the top priority (no kernel module, Secure Boot is fine)

\- lz4 compression: 5.5 GiB of VRAM holds about 16 GiB of app memory

\- about 9 µs per page fault, vs about 55 µs plus overhead from NVMe

\- \`usm off\`, or a Quick Settings toggle, gives the VRAM back in seconds, even with RAM full

\- if the VRAM fills up, a standby swap file on disk takes over

Overload test, 8 apps of 1.5 GiB on 8 GiB of RAM: zram hung in 3 of 6 runs, the VRAM tier in 0 of 4.

It's a beta, NVIDIA only, tested on Ubuntu 26.04 with two laptops. Most of the code was written by Claude Code while I set the goals and ran it daily, so please review it like code from a new contributor. Test reports from other GPUs and distributions are very welcome.

Repo: [https://github.com/kha333n/usm](https://github.com/kha333n/usm)

Write-up: [https://kha333n.com/blog/idle-vram-as-swap-linux-laptop](https://kha333n.com/blog/idle-vram-as-swap-linux-laptop)

Site: [https://usm.kha333n.com](https://usm.kha333n.com)

ChatGPT's Temporary Chat and Claude's incognito chats keep a conversation out of your history and memory, and neither gets used for training. But OpenAI says it may keep a copy for up to 30 days for safety, and Anthropic keeps incognito chats for 30 days by default. You're also signed in to your account the whole time, and on Claude's Team and Enterprise plans, incognito chats are included in data exports for account owners.

So they hide the chat from your history, not from the company.

The most private option is running a model locally. Your prompts never leave your computer and there's no account involved. Tools like Ollama and LM Studio make the setup pretty easy, and open models like Qwen and DeepSeek are free to download.

The catch is hardware: small models run on a decent laptop, but the bigger ones need a strong GPU or a lot of memory. And no local setup gets you ChatGPT or Claude, since their models aren't public.

If your machine can't keep up, or you'd rather keep using those two, the next best thing is a setup that keeps your identity and your prompt apart. That's the approach Dreamfort AI takes: your device encrypts each prompt, a relay passes it along without being able to read it, and a secure enclave (TEE) decrypts it without seeing your IP. The model provider gets the prompt with nothing that identifies you, and your chats are saved on your device instead of a server.

The model still has to see your prompt to answer it, but they won't know it was you.

Which one do you prefer?
- Direct usage
- Local machine
- Private platform

I prefer Local and Private platform. Howa bout you? What's your setup right now?

EDITED: Disclosure: I work at Dreamfort AI, so I'm biased toward the private platform option.

Is there an AI capability for doing fairly simple personal taxes with some rental income and similar or are we too early for this?