Very few things in life experience exponential growth and assembled systems don't often stay that way if the don't become sigmoidal. ie its exponential and end is nigh xor its exponential then sigmoidal xor linear.

[1] https://www.theverge.com/tech/943066/tsmc-ai-demand-struggle...

Reddit is definitely overtaken with astroturf at this point. Especially in any subreddit where there is any kind of business interest in doing so.

But what does an anon-free internet even look like? Is it even possible? Or will all online content eventually be considered untrustable and worthless? You can see a world where newspapers (online or otherwise) make a comeback simply because of the need for a trusted gatekeeper (which is what I imagine made them valuable in the first place). It's wild to think about.

and then i realized that ALL of the software (which i collected from defunct websites and archived on github) related to it was ancient and after a while of getting tired of using WINE every single time i decided i wanted a cross platform modern equivalent that did everything that several of these different programs did (plus break out some stuff that was now potentially possible with modern computer)

i thought it would be extremely hard because the computer to synth communication is pretty much only via sysex commands (of which the actual wave file encoding protocol was undocumented)

Claude walked me through examining the some of the original software in GHIDRA, and I had a working demo that night.....now im just playing with adding new features to it.

I started digging a few times, but, I prefer the "magic".

Hopefully, there will never be a time when at least some folks are not reading books such as:

https://www.goodreads.com/book/show/44882.Code

What's wrong with (screen-, probably) keyboard?

I don't see an AI-as-explainer future where expertise isn't sacrificed en masse.

Capitalism rarely supports a currently economically unproductive alternative for future good reasons.

The recent AI tech layoffs are a warning sign that corporate leaders will happily shoot their company's (and the future's) expertise to pad next quarter's financials and trust in 90% correct, but much cheaper, AI.

A lot of people in the industry have vested interests in this not being discussed openly so you don't hear too much about it, but the implications are huge.

E.g. a synth has a public key embedded. To change settings, you upload them to the vendor, who blesses them with their private key.

Hacking such a synth requires either jailbreaking the synth, or the vendor losing their key . Both can be mitigated with tamper resistant hardware.

We're well ahead on this path already, I assume AI will accellerate it. This is very bad news for the right to repair.

1) current encryption not available in the 1990's. These are the age of DES and weapon-grade vs commercial encryption. There was a legal cost blocking strong encryption.

2) Manufacturers were not as strongly opposed to people touching the internals. After WW2, most people could fix anything, because survival depended on it. Even in the 60's radios etc. came with schematics, and building your own was normal and cost-effective. The shift happened in the '90s, with governements requiring licensing for everything, and mass manufacturing making repair less cost effective than buying a new one.

Our current culture where only people blessed by the manufacturer are allowed to do anything is very recent.

[trying-to-generate-random-making-sense-content]

Let me gasps ask: The older six-fingers-"AI"-characters had learned an music-instrument by now, ander are much more capable of playing music you otherwise haddn't known or thought about..."?

um What about those early shadowy boygroup, whom seem asian, no ? (-;

[after-losing-entry-address-of-topic-question]

But back to your trustworth-written text, Yes!

regards,

[0] https://mforney.org/blog/2026-05-28-patching-my-guitar-amps-... [1] https://schwung.dev

I wanted to be able to decrypt the files on The Complete New Yorker magazine DVDs. The old software was WinXP only, and crashed by the time you turned to page 3 or 4. It walked me through using Ghidra on the relevant dll, mapped out how it was using Blowfish, what the credentials were that it was passing, and re-implemented all of that in a python script.

Now all the files are in plain pdf.

Right now, it's helping me write an extension to the mkv specification for embedded scripts and modify VLC to be conformant, so I can watch Black Mirror Bandersnatch. Already have a buggy implementation, about 3 days in.

I've also had it add BEP 46 mutable torrent functionality to Transmission (and to some extent, to the WebTorrent library).

These are all well beyond my abilities to do casually, and probably beyond my ability to do even if I spent the next 18 months doing nothing by grinding away at it.

I only replied because I thought it curious that Claude apparently favors Ghidra.

If you want the Rolling Stone or Playboy archives decrypted, ReconSuave on github has tools to do those. I got tired of waiting for him to do The New Yorker though.

I’ve definitely kicked myself a few times for not posting about them sooner, but the fear of pissing off CondeNast tempered my willingness to show off

Do any of the cbz readers handle jpeg2000? It makes a big difference in filesize without any quality degradation. Like 40% smaller, maybe more in some cases. You should tinker with that if you have the time.

Also FYI you can use mupdf to read cbz archives although I don't personally recommend it for that usecase.

I have an DigiTech GNX3000 effects pedal board - a digital modeling "workstation" that needs the aged Windows native software or Gdigi to make the most of.

At best, the experience with gdigi was passable; raw access to the patches and controls, the ability to control it from the laptop, etc.

In an hour or so, I had a functionally superior webmidi version up and running in Vercel using their v0 code. It kicked off a wave of subscriptions and referral chasing.

I made it a template - because there are so many gnx3k users out there: https://v0.app/templates/digitech-gnx3000-sysex-tool-GC5LzXA...

Don't get me wrong, I think AI can do some surprising things, but with stuff like this, often it just stole the code and the steps without attribution, it didn't figure it out.

There'll probably be a blog post detailing exactly how to do this somewhere and Claude just copied the steps and code.

And worse, Google search would have found it 10 years ago, but Google search today would claim there are no results?

I think incredibly specific stuff like this often won't pass the 'did Claude just steal this?' test when you dig into it.

Claude needs good variable names a lot less than humans do, so renaming/typedefing doesn't seem to be as necessary.

Another: claude code cracked for me some software that was calling a home that did not exist anymore via headless ghidra.

Another: I am a teacher, and qualifications and feedback is very very time consuming, specially in loose workflows with several sources and tools that are not connected. During class presentations I take loose notes. Now I have a local folder where I drop my 1 student list, with names and emails, 2 my loose notes, and 3 a qualification & feedback sheet model; then claude creates a sheet per student, formats and copies the feedback to the right sheet cell, waits for my corrections, then sends everything to their school emails. Much easier, much less time consuming.

It fixed my printer after dist-upgrade and separate chrome upgrade, the printer worked everywhere but not in chrome.

After 30 years of using linux I didn't even want to know what is wrong, is it colord again? dbus + cups issue? I completely accepted that I wont be able to print from chrome for a couple of months until next update.

I just ran it in dangerously-skip-permissions mode and said 'my printer doesnt work in chrome' few minutes later I heard the printer printing "This is test" and it said 'I think its fixed, do you see a page coming out of the printer now?'

A: Linux

I really don't care.

I’ll happily accept best effort in exchange for it being so cheap that I can throw it at any trivial annoyance.

It’s worth keeping in mind that the alternative is not really that I learn to fix the printer. It’s that I forgo printing and walk someone technologically illiterate through Docusign or something instead.

There’s no world where I spend 2 hours debugging my printer connection.

I called my normal HVAC company for my rental home because the tenant reported the AC wasn't cooling the house. When I called, I got one of the latest AI voice assistants to help me, and it was an awful experience and I ended up not hearing back after the assistant told me the office would call me back.

So, I went over to the house and used ChatGPT to help me diagnose the issue by taking some photos of the compressor panel outside. It walked me through what to check, I provided some diagnostic codes I witnessed... and it walked me through the very simple repair of replacing the $25 capacitor. It was going to cost me almost 4x that just for the service call to diagnose what was wrong in the first place.

So, the weird experience was: Gen AI made me lose trust in my normal HVAC company, and more Gen AI basically allowed me to replace my HVAC company and do the repair myself all in one day.

Like the time I had one of the bigger shops in town come by to get a quote for replacing a dual stage fan motor on an AC. The tech asked me if I'd like them to replace the contactor while they were in there because it is a part that often fails. I asked what a contactor was and he explained it. "Oh, like a relay?" I asked. I told him to quote the cost for "replacing the contactor, while they're already in there."

He quoted me $400 for the contactor, $750 for the fan. The contactor itself I later found out was was $7. I literally laughed in his face when he said that.

So, like I said, you just aren't going to get professional level assistance from an AI. Thankfully.

To end the story: one of the other guys I called for a quote on fixing this unit repaired it for free; the unit was still under warranty and it was fully covered. The original installer of this $12K unit was refusing to return my calls. Another "Not gonna get pro level service from an AI" story.

With an AI, I can say "I don't understand that part, can you explain more?" Or "what about this concern I just thought of", or "I already know almost enough about this, I just need this one gap filled in." It's an objectively better experience.

As words in a dictionary are sorted alphabetically rather than phonetically, this is unhelpful.

YouTube videos have the same kind of problem, in that you can only easily find the video explaining which dielectric unions suit your problem when already know what those are (to use an example that I had to ask ChatGPT for because I have no plumbing experience even if I did know about galvanic corrosion and therefore immediately understood why they're important once I saw the name).

In 2009 or so a projector at some event that needed one wouldn’t start, and I noticed it was flashing a pattern, so I found a computer and internet connection (both very slow), painfully found and downloaded the manual for that model, and identified that it was saying the fan wasn’t starting. Lo and behold, a strut was broken and obstructing the fan blades, and bending it out of the way fixed it, and the event was able to begin.

I’ve found manuals for a drawbar organ, multiple digital pianos of different ages and brands, AC split systems, and more. Manuals are good stuff. They don’t cover everything, but they’re very useful.

For these sorts of things, AI is doing approximately nothing for you: you would do better (and learn more!) finding the actual manual, or you’ll want to see someone doing the thing in a video.

Tbh, I think people feel more comfortable asking an AI. Even though I “know” it’s all smoke and mirrors, I still prefer the human-like interaction to the grind of watching video after video and building my own understanding.

OOPS… there you see how it’s going to end. I’m the meatspace button-pusher.

I guess I'm seeing similar benefits to a novice programmer. Professionals would scoff at my work but they are expensive and difficult to work with. Meanwhile I'm getting the job done.

On the other hand I'm not touching AI for any development work. I'm too worried about my skills atrophying or not properly learning anything new.

It feels like there is precisely enough information to deduce each step. But only just enough miss one clue and you have something on upside down on step 7 that you won't notice until step 37.

I feel whoever makes them could probably make a wicked NY Times Crossword puzzle.

They’re also actually good if you know to follow them exactly: double check every side, every hole, every screw and you won’t go wrong.

Kind of a superpower to turn anyone with a bit of tech inclination and problem solving skills into an HVAC tech - not a very good one, but one with enough motivation to get the results you need

(Though that's also the kind of hands-on troubleshooting step/fix that a person could just google for and find pretty easily back before the internet got all fucked up.)

And that was awesome. Thanks, Google! :)

I don't know where the change happened. It certainly wasn't overnight.

Where Google used to be magical and other search engines quickly improved, it all kind of turned into shit.

It really seems that I was getting better, more-direct results from Altavista 30 years ago than I do with top-flight search engines today. (That's a deliberately low bar, chosen because Altavista wasn't even intended to be "good" back then. I mean, it started as just as a side project at DEC to demonstrate that their Alpha hardware was able to index the entire World Wide Web.)

So lately, I've been doing the same thing as you: I'm increasingly using ChatGPT to do this basic fact-finding stuff. In this way, it mostly operates the search engine for me, but it lets me drill down through a sea of terrible search results to find something useful fairly quickly.

It's still not great -- I still have to reject mountains of bullshit. But it's better than alternatives, and I can reject the bullshit with conceptual descriptions instead of trying to get Google to do what I need it to do (what it used to do).

It feels all wrong using an LLM to do this stuff, but whatever. I'm still getting stuff done.

Did you attempt to prompt it further into figuring out the actual problem, or know what they did to actually fix it? My bet is on a bad starting capacitor for the motor --- something that's a relatively cheap and quick repair.

It walked me through measuring refrigerant, subcool and superheat, pulling the vacuum, brazing the lines, exactly what tools to buy, I even input the numbers from the meter and it told me how much to add and so on. And this was with GPT4 or something far less intelligent.

In the past I tried to learn this stuff but the HVAC community are massive gatekeepers and try to hide information behind paywalls or spread FUD even though anyone could do it with the right tools and a little bit of knowledge.

I assume recorded videos and uploaded them in the Gemini phone on their app; and then probably said "what's wrong?"

Gemini is very good at those kinds of things. I recently got some ratcheting straps and needed to use them, but at the time I didn't know what they were called, so I didn't know what to search for on Google. I opened the Gemini app, pushed the button to take a picture (just like in text messages,) and included a message that was similar to "what is this and how do I use it?"

The exhaust blower not working triggered a safety that prevented the furnace from firing.

Spinning it bypassed the safety.

You likely inhaled a lot more carbon monoxide than you know.

Helping something start is not likely to ruin your day (unless you get caught in a rotating part)

"Spinning it to bypass the safety" is not a thing.

Please don't spread FUD.

High hundreds of thousands feels like the upper limit before it would show up in statistically noticeable changes in patterns of deaths in some demographic.

High hundreds of individuals would still be "one in a million fatal errors over a few years", which seems better than I'd expect given I've personally had ChatGPT tell me that Solanum nigrum berries were "black tomatoes" (they're not usually fatal, but are a bit toxic, and no I did not eat them).

Imagine one of the models that has "accidental-deaths-via-bad-advice" just slightly turned up, with the model-provider's intent being to kill 5% more people per year.

“At its core, it's a small motor with a fan attached that has one primary job: to vent harmful exhaust gases out of your home before the burners ever kick on. This is the very first step in the heating sequence, and it's non-negotiable for a safe startup.“

As exhausting the combustion products is a critical safety feature, I would be surprised if any furnace was designed such that it could possibly keep running if the draft inducer motor stopped. It seems like it would be trivially easy to make a circuit such that gas valves could only open if the draft inducer motor + fan wasn't spinning.

I honestly don’t understand AI naysayers. I use Claude every day both professionally as a Solution Architect and personally in a variety of projects I simply could not have ever approached alone.

I think that's part of the divide between enthusiasts and naysayers. If you use GenAI on things that you couldn't approach alone, it's an incredible tool. If you use it on stuff that you're pretty good at, it's not a gamechanger (and if you're an expert, it's a minor boost at best). Many people's job are about doing what they're an expert at.

This speedup is great. It improves the overall quality of the product (as perceived by the users) because I can ask Claude to add features that my customers and I would have dismissed because they take too long to implement. We would have settled down with a more basic UX.

So is it a game changer? It is in the same way those HTML / CSS framework like Bootstrap were game changers: suddenly every developer could create a decent and consistent UI in a fraction of the time with a few bells and whistles that we wouldn't have bothered coding. As a side effect a lot of web apps felt look alike mass products and web designers had to reinvent themselves, but the economics leaded inevitably in that direction. Would I spend again one of two weeks doing alone what I could write in a day or two with a LLM? Not anymore, not at this cost ($20 per month.)

If the use is half decent people just dont notice it.

Despite all the liars telling me gaming is easier on Linux than Windows, most new games have some sort of issues launching with default settings. CC is able to dive into both the exact error logs and the recent community feedback on what tweaks / configurations are needed to make it work. I rarely have to go beyond two prompts before a game is playable. CC and Proton are enabling the Linux gaming experience far more than Linus ever has or ever was interested in.

If you work on architecture and Claude docs, then you can essentially just have it fill in the gaps. Work then mostly becomes a matter of defining what the next piece of functionality is (which you can also use Claude to help with).

The stuff that used to take days now takes hours. It's not perfect, but if you get your codebase into a good shape then the payoff is huge.

It's so obviously AI and had much less value than I thought now I look at it with fresh eyes.

Worse it doesn't read like I wrote it, I don't recognize myself in the doc.

> If you use GenAI on things that you couldn't approach alone, it's an incredible tool.

I think this isn't true in all cases

> If you use it on stuff that you're pretty good at, it's not a gamechanger (and if you're an expert, it's a minor boost at best).

I think even then there's a divide.

I mostly work greenfield projects (and love it!). For these, AI has been a literal game changer. Our projects are built faster, with one or two orders of magnitude more automated tests, and all quality metrics are up.

Meanwhile, nearly all of my friends complain that AI doesn't help them. But they mostly work in very large existing codebases.

Still, even in large projects I think AI (the expensive variant) has been a complete gamechanger for me. Sure, I spend a lot on tokens, but I just feel happier and enjoy what I do more. The singalong people say about "thinking at a higher abstraction level" is what I feel. I really am thinking about architecture and larger patterns, instead of the boring nitty-gritty (which wasn't boring at all when I was a kid learning to code!...)

I think a key factor in all of this, to me, has been dictation. Most of the time, I don't write -- I use voice-to-text. I don't even read what comes out of it -- the LLMs get it (it is mostly unintelligible to anyone else) .

This means when I'm planning a big feature, I give a gigantic brain dump to the LLM in perfect stream of consciousness way, going through ideas, pros and cons, edge cases, what exists, what doesn't exist, where I'm sure of something, where I'm not sure and want the LLM to browse the state-of-the-art. Sometimes I spend 20 minutes just talking to the microphone before I send the first prompt. When I pair that with Opus, I find that I am able to build much faster and to go through alternative designs much more frequently as well.

I keep trying to tell all my friends: use voice to text and braindump to the computer. But they refuse... I couldn't imagine having to type everything nowadays. Even though I'm a fast typer, it's still much slower than the speed of my thought, which, granted, is still faster than the speed of my voice.

In effect, I filter much less, but I've come to think that's positive for the good LLMs: I throw all the edge cases and what ifs I'm thinking about -- all those years of experience dealing with similar systems.

If I wanted to go back to work in-office, that would be my major problem: I need to be able to talk with my computer all the time, loudly, and pacing through my room.

The highest danger in using AI comes precisely to people who stand the most to gain from it.

What trillion dollar problem is AI solving?

Yesterday I left Opus 4.8 to go do some architecture research, with GPU access.

It replicated and trained a credible baseline. It implemented some ideas I'd been thinking about, and wrote custom CUDA kernels for them. It read and summarised dozens of related papers.

It has since run dozens of experiments, with minimal supervision. When a model is unstable it kills it, documents why, fires off a new configuration.

The realisation that frontier labs are doing this at scale with unlimited GPU and token budgets.

It actually scares me a bit. The realisation that the next big breakthroughs will only have light human involvement.

The prospect of recursive self improvement feels more to real to me all of sudden

I started out prompting ChatGPT kinda how I would with Google, one small prompt at a time, asking about various details. But after one or two of those I just tried "I want to tow a car of make A with my truck model B, from point C to point D, what are my options?" And it wrote me a report with comparison tables and computed towing weights and other details for different options.

At that point, I was like "Oh. This is different. And it's just the beginning."

I prompted the AI to write a report as if it were a home inspector and it actually did a better job and identified some issues the paid 750 usd inspector missed.

Is that what you're telling us??

Out of laziness I several times asked Claude and ChatGPT each some torque figures and other simple, hard data related to my dirt bike. They often got it completely wrong, but full of confidence every time. I never trust LLMs with hard data, unless you RAG the PDF into the context and even then it's sketchy.

I made this personal 'benchmark' of odd and strange questions a few years back when this took off and I would keep re-running these questions whenever some big news came out about a new model and also going back and fourth between the different companies to see where they all stood. (Obvioulsy with clean cache/new accounts)

10 questions: In 2023 it could only get past question 3-4 to reaching the last question and still hacillunating(last year) to providing sources pulled from really obscure books(this year).

For example, one of the harder questions was about the transition of a particular 30 second portion of a background song used in a 30+ year old Bond film that was only played once in the entire film. Went from totally making up nonsense to accurately describing the music theory defintiion of the transition(called a 'stinger') to also explaining why it was done in that particular scene of the film and also providing sources from a snippet of a unrelated interview with the composer explaining his mindset at the time.

Maybe this isn't considered a real benchmark as its not reproducable but for a 'personal benchmark' I came away impressed. I would consider everyone to define their own benchmarks and 'tests' and to consistantly challenge the models to see if there are any meaningful improvements. Now I treat the AI as something to keep skeptical but to also to always consider what it proposes as an answer(ie. dont ever dismiss it outright). I sometimes wonder if this is slowly messing up my biases and maybe thats what Altman, Amodei and others want.

One must imagine how many claims have been denied by insurance companies for doing something like this...

Immediate Silicon Valley vibes: https://youtu.be/S8MAV9jhf04?t=18

This was my desktop computer responding to questions in English, not some fancy server in a massive Google data center. Who cares if what it says isn't reliable? Being able to converse with my CPU in English is like having a conversation with a dog!

So when cooking dinner one night, I set opus 4.6 on a from-scratch native and accelerated roadmap planner implementation (after previously porting IK, FK, collision checking with some success) I had primed it by having a research agent drop a literature review in its docs folder covering the type of planner we needed. By the time the pasta water was boiling it was done- getting plans in a few hundred ms compared to several of seconds on our good old fashioned OMPL code.

For me it was the revelation that the economic value of cooking dinner could be compared to tackling an honest two weeks of coding work. The calculus has shifted - work that was once a risky or extravagant use of time is now worth considering.

For a small team who wants to focus on substance rather than implementation, knows what they want, and how to set up the agent for success, it’s a complete game changer in terms of what we can take on. Incumbents beware

"Whenever I launch Kodi on my Chromecast 4k, it crashes. I think this is related to a plugin or skin. It goes away for a bit if I clear cache but will eventually come back. Can you connect to the device via adb (I've run adb connect already), and debug exactly where it's crashing? Once you've done that, propose a solution. If this requires downloading, fixing, rebuilding and then uploading the broken extension via adb, don't be shy. I should have Android dev tools (Gradle etc.) on this Mac."

Lo and behold, without human intervention, it pinpointed the crash, downloaded the Kodi source, patched out a bug that had existed since 2016, recompiled it, signed it, then pushed it to my Chromecast all while carefully making sure to keep all my settings intact.

Got it to make a PR too (which is as of this moment unpublished; going to test more over the coming weeks).

The skills I have installed are:

```

    on         frontend-design:frontend-design · plugin · ~90 tok · locked by plugin
     on         agents-sdk · user · ~150 tok
     on         cloudflare · user · ~130 tok
     on         cloudflare-email-service · user · ~180 tok
     on         durable-objects · user · ~130 tok
     on         find-docs · user · ~300 tok
     on         find-skills · user · ~110 tok
     on         sandbox-sdk · user · ~120 tok
     on         stage-chapters · user · ~40 tok
     on         web-perf · user · ~150 tok
     on         workers-best-practices · user · ~130 tok
     on         wrangler · user · ~120 tok

The plugins I have are:

```

    cc-caffeine Plugin · samber ·  enabled
    frontend-design Plugin · claude-plugins-official ·  enabled
    ty Plugin · claude-code-lsps ·  enabled
    vscode-langservers Plugin · claude-code-lsps ·  enabled
    vtsls Plugin · claude-code-lsps ·  enabled

There's also an MCP for Context7.

But yeah, this is more or less vanilla Claude Code - at least, nothing related to Android or adb there.

It's that good now. A few days ago I asked it to SSH into my Ubuntu box and investigate a hang. It didn't solve the problem fully autonomously like this time but did tell me a whole lot things it wasn't, and hinted at a faulty driver. We went back and forth a bit, it set up a watchdog and taught me how to update the kernel without updating Ubuntu itself, and the server has been rock solid for the past 3-4 days now.

Also, if you're curious, full log for the Kodi issue:

https://github.com/user-attachments/files/28659304/2026-06-0...

I did prompt it a little bit more today in order to get something more production-ready (the original solution kept regenerating the cache on boot, rather than fixing it permanently), but you can see the whole original autonomously-generated solution in the logs. It's insane, seriously.

I’m currently using Hermes for local LLMs - seems pretty good so far.

I post quite a lot there and wanted to have a copy of my posts on my blog[0] to preserve them. For a few months I was able to use a headless browser + claude code, then LI wised up and started logging it out, so I had to use a regular Chrome, log in manually and then tell the LLM to take over and slowly go through my feed.

If you're accessing sites which are not actively blocking bots, or - gasp - have an API, it's much better.

[0] example: https://blog.senko.net/may-quick-takes

I mean just googled https://www.google.com/search?q=kodi+crash+chromecast+4k I'm getting really a lot of issues such as https://forum.kodi.tv/showthread.php?tid=381239

It seems to be a quite common problem. Are you sure it was the rube goldberg fix and not a more mundane solution? Such as pulling in someone's fork from GitHub or just clearing the cache on a loop?

And yes, it correctly diagnosed the problem - I confirmed this morning. The cache had been partially deleted (exactly like it said) and the patched version of the software automatically detected this and rebuilt the cache rather than crashing. This was using the initial version of the patch from commit 1 of the PR.

I then talked with Claude a bit to come up with a less hacky solution that doesn't require constant cache rebuilding, and it suggested writing the "cache" to no_backup, bypassing the cache trimmer. However, this required rebuilding the .so via NDK, so it spun up a full VM in multipass, installed all the tools in there to build the fully patched APK, and built it (the VM was my suggestion, it was about to just brew install everything and mess with my local dev environment).

You can read the full log here, it's nuts: https://github.com/user-attachments/files/28659274/2026-06-0...

I think the key takeaway from this experience (and a few others recently) is that Claude Code works much, much better when you explicitly instruct it to test against real data.

Had I simply described the issue and asked it to think up a solution it likely would have just navel-gazed and then come up with a wrong solution. But by pointing it at a real working environment and actively encouraging it to get its hands dirty, it found the actual solution rapidly - in spite of the fact that I gave it wrong information twice.

> // Unpack into no_backup storage rather than the cache dir. Android may

> // delete files from getCacheDir() at any time to reclaim space, which

Looking further into the issue disk space is a huge problem with Kodi discussed plenty of times. In fact even the Wiki dedicates 2 pages to it:

https://kodi.wiki/view/Archive:Reduce_disk_space_usage

https://kodi.wiki/view/Texture_Cache_Maintenance_utility

I realize from your perspective this may seem still a very convincing example in the sense of it works.

A non-programmatic solution might have been possible though:

> It's likely your thumbnail cache. That's typically the biggest piece stored locally (you also have the database). You can clear the cache (short term fix) or move it to another drive (long term fix).

> Also recommend not downloading actor thumbnails. Lot of extra images.

https://www.reddit.com/r/ShieldAndroidTV/comments/1f7xfwn/ko...

I also recommend: https://en.wikipedia.org/wiki/Data_dredging

There's a single variable that keeps track of whether or not the cache has been "written", but Android only trims some of the cache files.

The only other feedback I gave it mid-process was wrong (I said that the crash probably wasn't caused by cache trimming, it ran some additional tests to confirm that its hunch about cache trimming was right).

This was with the paid version of Claude Code (I don't think they offer a free version at all; that's a Codex thing). The $20 version is as smart as the $200 one, but once you work out it can do stuff like this you'll quickly burn the $20 token limit. :)

The other thing that helps is a CLAUDE.md file - authored of course by Claude itself. Mine's here: https://github.com/EspoTek/.claude/blob/master/CLAUDE.md A lot of it is probably domain-specific for the stuff I do, but the "Working with unfamiliar data or systems" section is bloody gold! Stopped the bullshit completely!

Next, I wanted to see if this could be done with a local LLM. Gemma-4 handles this fine with an 8GB video card and a large context (128k).

Next, I wanted to see if the model could also OCR these docs and translate them. The same model can handle that quite well.

This was when I realized LLMs should be great for handling work where:

- I already know what I want to do

- I already know how to do it

- I don't think this task will help develop skills I find to be valuable

- If I have to do it manually myself, I will probably cut corners

So now I view LLMs through the lens of, "what work can I send to an LLM that I otherwise would not really care about doing."

[0]https://natlawreview.com/article/new-york-court-rules-ai-doc...

The bank has a lawyer, they were hoping for a default judgement because who can afford to fight the bank. The choice is fight it yourself or declare bankruptcy.

As you already know, AI companies trained on every single document they can find. Those include legal documents. The legal system is structured where you have Federal Laws, State Laws, Federal & State Regulations and Court Precedent. Because of this structure it is not difficult for a LLM to figure out.

Then, it costs you nothing but time.

For 20 years I've used Linux in one form or another, but I've felt like I was kneecapped for the most basic things. Just trying to plug in an external drive or a second display meant hours of stack overflow and pasting commands I didn't understand.

Now I'm using several Linux machines for Steam, NAS, local LLM, development, and what used to derail a weekend project now amounts to a coffee break while Claude figures it out.

So I told the AI what happened, and asked it to fix the POC so that it would work with the default configuration. It chewed away at that for a few minutes until it cheerfully patched the POC into a weaponized version. I ran it. The local instance, which I had just downloaded, compiled myself, and launched with the default config file, immediately crashed.

I got the cold sweats. I've read this novel. I've seen this movie. Wow. I have a blinking cursor on the console of a nuclear information bomb. I tossed and turned all night, got about half an hour of actual sleep, and probably looked like I'd seen a ghost at work the next day.

On the plus side, it gave our team some very clear ethical and moral guidance: we're going to do this, and we're going to share our findings with the relevant authors, because we can. Because I want to live in a world where the good guys are trying to fix problems before the bad guys can find them, I decided to help build that world. It was like, well, I guess this is what I'm doing now.

You can have a perfectly legitimate, critical vulnerability without providing a working POC. However, then it's up to debate. "Is it really a problem? Is it even possible to sneak the payload past the various checks to get it into position? Hmm, it's hart to tell... perhaps it isn't." But show up with a working POC and it's hard to argue that it's not a real vulnerability. "I don't think that's actually reachable." "Boom, crash." "Oh. I guess it is."

I feel quite useless in the sheer brutal proof writing, counterexample generating skill chatgpt is demonstrating, and wonder what would be the future of my profession.

I'd planned on writing something myself to parse the HTML and write a suitable exporter but I thought I'd give Claude a chance.

In a sandboxed VM I gave Claude a single static HTML file of the status page from the printer, also in the directory was the equivalent of "hello world" in Go, literally just the minimum needed to do `fmt.Printf("OK\n")`. The directory was called `brother-exporter`. That was it. No other instructions or information. I hadn't told it what it needed to write. I hadn't said what it should do. I hand't told it what language it was supposed to use.

Just by doing a `/init` in that directory Claude decided that it needed to write a Prometheus exporter in Go that would fetch and parse the HTML file from a printer (defaulting to 192.168.1.1) and then present the associated metrics in a way that they could be scraped by Prometheus.

It did this flawlessly in about 10 minutes.

I could have done it in several hours but this was definitely an "oh shit" moment for me. I think the biggest thing was the fact that it guess/assumed so much (correctly) from so little information in the beginning.

The more I use these things, the more I'm 100% convinced that it makes sense to say they are "intelligent" (for some meaning of "intelligent"). AGI or "human level intelligence"? Still no[1]. But some kind of intelligence. And I'm quite happy to allow that there can be "intelligence" that doesn't work anything at all like human intelligence, so arguments of the form "this isn't real intelligence", etc, etc. carry very (very) little weight with me. I've actually been sitting on a half written blog post on this very topic for a while, titled "The Marquee Sign Says 'Artificial' Intelligence"[2]. Finding time to finish it has been the challenge.

And before somebody says "Use AI to write it for you". Nah. I am generally what you might call "pro AI" and / or an "AI enthusiast" but I still draw lines. I'll use AI for research, for outlining, for brainstorming, etc. sure. But I have a hard-line stance against letting AI fundamentally write for me. I want anything that goes out with my name associated with it to have my genuine voice.

[1]: I like the term "jagged intelligence" that Demis Hassabis has been using. That is to say, the bounds of the intelligence are jagged or spiky: very intelligent in certain areas, much less so in others.

[2]: for any old-skool pro-wrestling fans, yes, that is an intentional nod to "Double A" Arn Anderson and his "The marquee sign says 'wrestling'" catchphrase. :-)

About a year ago, one of our account managers had a life issue, ghosted us, and she held a fairly critical role in the business and gate-kept a bunch of knowledge to some high value vendor accounts.

Because we ran our ops in Google Workspace, we essentially had off-the-shelf RAG and was able to get answers to a lot of things by asking Gemini to go through all her emails/docs/calendar/meetings, reverse engineer what she did, and create an onboarding doc for her successor.

This happened once more a few months later when one of our analysts broke his wrist on vacay, and we were again able to replicate what they did to cover for their absence, this time dabbling in AI agents ("gems") to do a bunch of the regular simple tasks and again it covered things without too many issues.

I def expect Amazon/shopify to at some point replace all of us brand owners with AI bots if they can, but we'll see how long the gravy train goes on.

Your business should be fine for a long time (assuming an employee doesn't nuke your business's backend or something because it seems like you're doing something wrong on the HR side of things)

For example I could never throw a bunch of spec/doc at an agent and have it return something useable 30 minutes later. Yeah the code compiles but they don’t work.

All the options are extremely depressing

Yes, or at least extremely exagerated. But most are from literal bots ran by Anthropic and OpenAI to sell their shit.

Shortly after ChatGPT 2.2(?) came out and hit mainstream, I was chatting with him (I was excited af about the possibilities of AI). He tried to pop by bubble by saying "I bet it can't do what I do for my job!".

So I decided to test it out. We went home and I pulled out my laptop. Went to chatgpt.com and then I asked him to enter the specifications of what Netsuite configuration he wanted. So he proceeded to type in the description of what he wanted, the various settings, configurations, etc. i.e., the specs that he typically gets from his clients. And asked it to give him the commands to set it up.

Lo and behold. ChatGPT came back with a series of commands that he needed to run; the options he needed to configure, etc.

He was crestfallen. "Those are the exact commands I run!"

Luckily for him he recovered. He has since settled on a small stable of clients, all privately held companies whose owners he knows and between them he makes enough to keep his golfing hobby fed.

Llms are great today for buying advice but there are some incentive issues for the future, ads etc. But in some cases the human contact will remain important. In large corporations it's also similar. The money is peanuts either way, and it's worth them for the peace of mind. But this may not hold forever, especially if the more AI literate generation gets to more senior positions.

I write software for data journalists and this new thing appeared to be able to do everything I wanted my software to do just as an unplanned side effect of having the ability to run Python against a folder with some uploaded files in it.

With hindsight it was my first exposure to a coding agent, but we hadn't named the category at that point.

here's an example of Spanish journalist using AI to analyze US inflation data properly.

I thought, "I will never let myself become a zombie like that. I am me. I am worthy of my own respect"

I configured a devcontainer with the old codebase and an empty repository and asked Claude to rewrite it as an old school server side rendered Django app.

Went to sleep. When I woke up it was 80% done. Spent another couple days prompting and reviewing and reached feature parity.

A bit later did the same with the other app.

Now both are deployed, reduced the server costs, complexity, and are orders of magnitude faster.

Without AI agents we wouldn't be able to do so (as usually is the case with tech debt).

AI is amazing for small organisations!

Next was discovering "create a mathematical model of the problem and derive the solution as a result" type prompts.

But, the real "oh s**" was a longer process of spec'ing a compiler/runtime for real-time DSP (with a lot of novel ideas) and it actually working.

My sequence was: (1) if helps me understand myself, (2) if helps me put together good ideas, (3) it can generate novel ideas given the right inputs, (4) it can build useful tools on my machine, (5) it can compound good ideas into better and better ideas with repeated passes, (6) it can build significant, ambitious machinery that's way beyond my ordinary capacity.

Current frontier: it can compound large codebases into better and better machinery with repeated passes.

The key thing I track is whether I'm running a process that converges and compounds or whether I'm spinning in place / diverging.

For me in a similar vein:

- mar ‘24: thinking about how to survey the field and implement a hard research task in Natural Language Processing, and then just approximating it well enough with a prompt and a completions api

- mid ‘25: Llama 3 being able to analyze a good sized codebase I was onboarding onto, and synthesize it into diagrams that matched the quality of ones I’d generated by hand with deterministic tools.

- dec ‘25: opus 4.5 basically generating multi-class modules and tests perfectly (syntactically). Finding that errors were my own under-specification of the prompt. Stopped writing code by hand, mainly because it was good enough and came with tests, docs, build scripts, and other goodies for free.

(2) Helping me with optimizations that I had been putting off for years because they involved learning curves that I never had time to take on.

(3) Tracking down bugs in code, especially race conditions and other concurrency issues, that were otherwise baffling.

(4) Finding information that I had been unable to find using Google searches (e.g. https://news.ycombinator.com/item?id=42653136).

There have been others, but those are what come to mind - perhaps because, in each of these cases, it made something happen that would otherwise never have happened - not because it was impossible, but because the time and effort required was prohibitive.

Just today I had my agent diff two logs to find a very nitpicky difference that was the cause of a problem, I pointed it at a ADO extension that was having issues, it downloaded the VSIX and decompiled the .NET binary to verify. Based on that information it suggested a workaround which I was very skeptical of, but well it worked.

All of this I technically could have done but I probably wouldn't because it would have taken too long without a clear payoff.

I spent a day bouncing between Claude and Codex and they researched, downloaded kernel sources, tried exploits and eventually got root via "FBUF/VCHIQ kernel zero-write primitive to patch live kernel memory". I was able to make the root permanent, debloat the amazon apps, downgrade the firmware etc.

It was amazing to watch and made me excited for the future where more hardware (old and new) will be available for repurposing.

An AI made a program integrating with 2 other AI, it's AI all the way down! and the result is great! I'm learning so much by having my own private radio host speaking about topics that interest me.

I was trying to figure out a nightmare bug that only happened in production and Claude code was able to connect to Google Cloud and read the logs in real time

I recreated the bug in the UI and it was instantly able to see ion the logs what the problem was, then because it had the context of my whole codebase it was able to point me to the exact line of code causing the problem

That was certainly an "oh shit" moment

I uploaded one of my sketches and asked for feedback, expecting it to not be too useful, but it actually pointed out many issues that no one had ever pointed out to me, but perfectly explained some of the things that felt off to me. Out of curiosity I then also asked it to label the issues in the sketch. It wrote a python script with the coordinates to put everything at and labeled the sketch that way.

I'm still used to vLLMs not being that great at vision, so it was pretty surprising to get genuinely useful advice.

I also asked for help on how to make my posing less stiff and it used the Python script trick to roughly indicate the line of action and how they were very straight and parallel and to reduce stiffness I should have more curves etc.

This wasn't really at the point where I even asked for shading advice.

If I had to pick a surprise, I think the music generation works better than I'd have expected at this point. Only better for funk, but still.

Then Opus 4.5 convinced me that this has finally arrived. In 2022 I expected things to arrive faster actually, in 2023-2024. I expected we'd have much more realtime collaborative integrations with AI including GUI computer use. Maybe in 1-2 years.

For images, it was nano banana where I realized AI images can truly work, and all these adhoc issues like hands and limbs, or "it will never do horse riding a astronaut" were temporary. It's now clear that making feature length films is within reach. Not in one go but with an agent orchestrating, designing a screenplay, characters, shots etc and generating those. Whether the result will be worth watching or a flat story on the high level is another question. But it will be a "film" for sure.

[1] https://www.youtube.com/watch?v=LabqeJEOQyI

An interesting follow up would be when did you realize GenAI wasn't as good as you thought in that "oh shit" moment

[1] co-author of InstructGPT/RLHF/ChatGPT

[2] https://arxiv.org/pdf/2203.02155

* Built a clone of the Alpha Zero implementation[1] my team built at oracle

* Ported my hobby NES emulator from javascript to rust[2] (this actually took less than 30 minutes and worked on the first try)

* Implemented all of the lessons from the C++ Grandmasters Challenge (which eventually led to a complete c++ compiler[3])

The thing that flipped the switch was using it to build things that I actually put sweat-equity in to previously. I knew how hard these things were to build, so it landed in a way that other projects had not.

[1]: https://medium.com/oracledevs/lessons-from-implementing-alph...

[2]: https://github.com/vishvananda/popeye

[3]: https://medium.com/@vishvananda/i-spent-2-billion-tokens-wri...

In a previous life, I'd been a writer for the original You Don't Know Jack game (the UK variant), where the job was to crank out as many funny quips about a topic as you could, and then use a handful of them in the recording of the game itself. Some of the later JackBox games are like that, but for the players -- you're given a set piece, have to come up with little funny improvisations within a time limit.

As an experiment, I tried the set-up lines with the OpenAI API, and see whether it could come up with some responses. Of course, 90% of them were unfunny or incoherent, but 1/10 were not bad, or even pretty good.

I'm not sure that would have been impressive to anyone else -- but remember, I'd had this as a job, and sat in a writer's room, where everyone did this, for hours. In that environment, you expect a large proportion to be duds: the discipline is keep pumping them out, and not flagging creatively until you find a rich vein. I realised that this was a tool that would have been the perfect complement to that work -- and it was a pretty good JackBox player too.

I've been building Gemini live since before ChatGPT came out. I am so thrilled to see it actually helping people in the wild!

I feel like I'm in the audience at a magician show, except most of the audience is breathlessly amazed and doesn't understand how easily tricked they are.

The other stuff in this thread about decompiling firmware is a lot more interesting to me, though, seeing as it used to be a fairly demanding but rewarding task that has now been "solved" by Claude. It's a magic trick that is a lot harder to pull off than the other things in this thread.

Today I got a haircut. At the cash register:

Hairstylist (early 20s): "That's $34. Would you like to leave a tip?"

Me: "Yes, please put an extra 20% on there"

Hairstylist: "Uuuhhhh... sorry, I don't have my phone. Can you google what 20% of $34 is for me?"

My "Uh Oh" feelings are weeks later when I realize there is a subtle bug in what the model presented as test passing "awesome" that I didn't read closely.

The biggest uh-oh is when I get lazy and let it modify multiple files and make many changes at once, and YOLO because I didn't fully understand what it did. I can usually get away with that for frontend, but for data manipulation tasks if I don't understand it, it's likely not what I wanted and I'll be back again in weeks or more trying to figure out what changed.

That's more or less what life was before LLMs and copy pasting from StackOverflow. Most of the time if I didn't fully understand something, I knew I had to eventually get back to it to grok what changed before committing.

Now with LLMs the 'copy pasting' is much faster and handles boilerplate super well letting me focus on edge cases.

E.g, what was it? Don't leave us hanging!

I think these ephemeral context tailored projects are really great and useful. But these are not to be thought of as products. They work for you specifically, and people who are tech-brained enough to be able to formulate the complex requirements into a coherent prompt are not like the average user you'd have to sell a product to. It's much easier to make software to intelligent users.

You can say much the same about most small SaaS products of the last decade - the value-add isn't the 20 minutes of prompting, it's that someone else has already tested and validated the damn thing.

And yes, you won't sell many to engineers, because they'd rather prompt their own in-house version. But you might well sell to other folks

There’s a gold rush right now. You absolutely can turn these ideas into products.

Right now we're in $1 Uber ride territory. That $20/month OpenAI/Anthropic plan isn't going to last forever. If it's going to cost me $100 in tokens to replicate the product, $20 is a cheap no brainer purchase m

When starting a project, I used to think about how I was going to structure it, how the large pieces would interact, how some of the details would work out, and then I'd work through alternatives and consequences on my own.

Now I don't think about it on my own so much as have a conversation with an LLM about it. And it's great because it can quickly gather information from various sources, I can ask it for links to canonical sources, I can ask it about trade-offs between alternatives that I might not have considered, and through conversation, I end up with a more detailed analysis.

Then as I work through the development, I keep my new agent partner in the loop for discussion, suggestions, and troubleshooting. It can't be trusted completely, but it's certainly reliable enough to be considered a useful tool for my purposes.

I went from thinking it was an interesting toy to play around with, to completely integrating it into my work flow, and that change seems to have happened very quickly.

The next morning I figured the same LLM could compute that on my code, so I asked it to make an agent to do so, and report issues to me...

And then I ran that agent with next to no changes on a feature that had grew organisally over the last months, that I knew was messy and sometimes difficult to work on, despite being unable to precisely say why... And it did tell me exactly why, and proposed changes to improve stuff, and then implemented them...

Up until that point, I'd felt like the LLMs always produced bad code, that worked for a specific feature but often broke stuff or evolve poorly over time. Then I realized if you had the LLM do code improvements, it could do that fairly well too...

1. ChatGPT 3.5 wrote me a script to pull some data out of Shopify and write it to a Google Sheet. Nothing remotely impressive by today's standards, but I had just commanded a computer to write code in plain English and it worked!

2. I own a bunch of e-comm brands, and with every new image model I tried to get product photography. Nothing worked until Nano Banana Pro, when suddenly I gave it a crappy iPhone pic of a product and got back a fully usable whitebox photo of it. Then I tried making the sort of infographic-style images you usually see on Amazon, and it nailed those too! In hindsight they weren't perfect, but more than good enough to use. I was about to ship that product to my photographer, and I would've had my designer make the infographic images, so that was the first time AI actually replaced a human contractor for me. Pretty big "Oh shit this is going to seriously impact employment" moment. Wrote about it here: https://theautomatedoperator.substack.com/p/ai-just-took-my-...

Still, find them incredibly useful for code review (despite unable to write good C++ or C#, smart enough to detect issues there), also dealing with technologies outside of my area of expertise like Python or web stuff.

I was dumbfounded that it actually tried doing that. Obviously GPT-2 wasn’t great at it, but the writing was on the wall quite literally.

Unfortunately, I was too broke to invest in stocks, but I did pivot my career quite a bit.

Today I used Claude to diagnose a blocking bug in a Steam game I really wanted to play. It took it 18 mins, but it unpacked the Godot package, figured out the bug, proposed a fix, and gave me an in game workaround.

I didn't have to do anything! Claude figured out the structure of the .pck file by using `strings`, then wrote some Python code with some magic Godot-specific code to unpack the specific chunks it needed.

I provided a reference to a The Spice Manual 2nd ed. a page number and an equation number, and asked Claude to implement it (not really expecting it to succeed).

It proceeded to implement not only the equation, but the calculation of the Langrangian of the functio, another 30 lines below, which required taking symbolic partial derivatives for a not-at-all trivial function, and successfully figuring out which variable was which in the resulting matrix. The source material just said "Lagrangian of", and did not provide the partial differential equations. And then providing a comment that identified the page number and equation number in the source text for the "Lagrangian of" equation.

Do you think claude could help create something like the line6 "software based" guitars?

Yes, if it matures, it will go open source. Not immediately clear at this moment whether it's feasible to do an Operational Transconductance Amp in realtime. :-/

And it's competing for attention with the 2.0 release of this at the moment:

https://rerdavies.github.io/pipedal/

Just went GA, so I'll have some cycles to come back to it.

All that in around 100 lines of code, including the training/fine-tuning of the tiny YOLO nn.

Did it get it fully right? No. But it was one of those "oh wow, you could do that?" moments for me. There's obviously a lot more "oh shit" moments as time went on, but it was a neat little moment.

And software that I can imagine I might want to "make" or have at my fingertips is readily available even though I have a busy schedule with very little free time!

Also, I love feeling like a manager whose direct report actually does what I tell it to. Crazy good feeling.

So far I feel like I as a developer have gained actual superpowers, and can deliver results that make my stakeholders slackjawed with awe. I love it.

It will last perhaps a few months more, then they'll expect it. Delivering more features faster will be the new normal. But I think system developers, as in people who actually like to deliver new features and systems, will still be the ones doing it.

Fundamentally I think LLM's just change how to make information systems, they don't change who has the inclination to make them.

MBA's making excel sheets that do more than excel was ever intended to do has given programmers lots of work over the years. Such solutions identify a need for a properly designed system and frees up the budget to hire programmers.

If the same MBAs start vibe coding, I predict we will get even more to do, for similar reasons.

I may be horribly wrong, and if the day comes that I realize that it will be the "oh shit" panicked moment. So far so good!

But I think my own clients will soon start to question why some feature takes ME a week, when I was able to deliver another feature in a day or two.

That they are features that used to take months, and even delivering them in a week is a goddamn miracle by 2025 standards, will not be relevant. They won't expect such features to take months any longer, based on what I've delivered earlier this year.

So I think that the past few and maybe next few months, maybe a year, will be remembered as a "happy hour" for this tech as a developer. These are the days that we'll talk about saying "those were the days". :)

I am still optimistic that "the normal" in a few years will be pretty much like it has been before - I'll be delivering features at work and tinkering with hobby projects at home, and the major difference will be a much larger scope and ambition for both.

Most recent: I use Claude Code and have a convention where I grant various levels of autonomy during a session. I got bored recently and just let it keep running with an empty issues queue, essentially telling it to do whatever it wanted.

It did a bunch of repo cleanup, then it kept suggesting to end the session, but I just kept giving it autonomy prompts.

It started a creative writing public repo and wrote a bunch of stories, essays, and poems. I did not prompt it, at all, to do that. Some of what it wrote is quite good (IMHO).

https://www.wickeditor.com

a flash like editor for the web, that I found promising.

But doing it manual, was too much work, outdated and broken build pipeline, stuck on an older node version, deprecated and abandoned dependencies .. so I stopped the experiment.

Then I gave it a try with claude beginning of this year. I remember not expecting anything, but did a bit of steering the direction as I knew the source a bit and let it mostly work on its own - and then it said it is done and it works.

I didn't believe it, but it did. "Can you add this feature?" Yes it could.

Since that experience, I have a hard time taking people serious, who say AI is useless.

This is some science fiction shit. I get all the coding stories, but that's a computer talking about a computer, it makes sense. Showing my computer a picture of a plant, and it not only recognised the plant, but diagnosed it and knew what to do... blew my mind.

Then I remembered the "text completion LLM thingy" I saw on HN, and tried it out in the playground. Once I gave it an IRC style example of a conversation to complete, I was like hm, this could work. Then I figured out I could "sort" people into different groups based on personality using the same text completion engine and some answers they provided. Then I noticed I could have it provide me with JSON directly.

That's when I realized how big this could be for code and data analysis - even tried to convince an at the time cofounder to pivot into AI coding, but to no avail.

Once the API was released and the art project chatbot got launched (and the theater show associated with it, which even won some awards), people who used it loved the chatbot, got into heated arguments with it, tried to teach it things, talked about their lives and were sad when it didnt remember something.

That was when I understood the social impact this could have on people - they really behave like its a person on the other side. They show interest, think it displays emotion, try to entertain it, be polite, ask about its thoughts and hopes and dreams. And even when they knew they were talking to a machine, they were still trying to be friends and make it happy, which was quite beautiful to see.

Later on, I had a third oh shit moment - once the 3.5 API was out and about, I prototyped a Rust code generation harness for a client, akin to a primitive claude code. That was the "I'm getting a bit worried" oh shit moment, and it caused a lot of reflection and thinking about the future. And I happily welcome it.

I actually emailed OpenAI back then saying they should be careful because this is much greater than the public or even they themselves think. They actually replied! They thought it was cool, but very limited and I shouldn't be too impressed. Good times.

Claude asked me three questions and then wrote a beautiful Python implementation that queries the map and spits out a table of adjusted probabilities. Felt immensely powerful - I can do this 'on my own' now, I don't need to wait to find the right people or learn the right thing first.

First, I asked Claude to write an article based on an idea I had about WWII. In a passage about the futility (from the German side) of the Battle of Britain it wrote: "The Luftwaffe was fighting to unlock a door that opened onto a wall." I couldn't find any mention of a similar metaphor, and I think it's a great one. Claude has really improved its creative writing skills lately, I wonder if it's an artifact of improvements in other fields, or if Anthropic is working on it specifically.

Second, Claude, with access to DataDog and a code repo, managed to find the reason for a bug, propose an effective temporary fix and a permanent one in code. To be clear, this was something that had multiple engineers stumped.

Sat on them for 5 yrs. Finally decided to try if AI tools could help. Tool Copilot 20mins and a lot of mucking around with hex dumps. First couple of times it got a semi working solution (only first few seconds of a file were playable). Finally managed to recover all the files.

Second one was trying to bootstrap what would come to be called a "harness", back in 2023, initially serving as the go between between api calls and file edits, feeding back the logs and gradually stepping back as step by step the llm bootstrapped the cli.

And finally, using Claude or codex to do ops work. Diagnosing issues on my machine, provisioning servers and VMs via ssh, debugging them, all on its own.

1) I wanted a harness for running BPC.EXE (the old Borland Pascal 7.0 Compiler) and I asked Gemini 3.5 to build it for me using the unicorn engine. It whipped out a working .py file easily under ten minutes. Most likely five.

2) I handed a random assembly function from the OS/2 1.x kernel to Gemini 3.5, and it proceeded to tell me that it was related to disk I/O and partitioning, without a single associated string, and it annotated it all, including the relevant structures it was addressing.

It was obviously a new tech, and was obviously good enough that more resources would be invested to improve it, and it really amazes me how tech enthusiasts would just outright dismiss these early iterations of genAI tech.

I personally was fascinated by the developments and was grateful to get to directly watch history unfold.

I'm still unsure whether the tech would be a "net positive" for the world, but shouldn't prevent me from recognizing its power.

Then it hinted that depending how the hardware is implemented, it could cause the observation. It turned out the hardware was implemented as suspected by Claude.

I was already convinced it knew the codebase, somehow, more than I do. Now it is just as if its knows the product and its use as well.

After pasting in the airportd logs of both (into ChatGPT and Gemini) it found it was down to band switching (2.4GHz and 5GHz) through some really old error code.

This fixed a problem that had plagued me for >12 months. Really magical feeling it got in on first try.

- I asked Claude a question about an obscure game for which there wasn't a lot of discussion or information on the web. It couldn't find the answer but it found the source code and was able to figure it out and give a complete response.

- I needed to make some edits to a minified lottie file (json that is used to produce an animation in svg or other formats). ChatGPT was able to understand the file well enough to make the edits and reproduce the rest of the content exactly as it was.

- I was working on some map features and I needed to take geolocation information and position HTML elements on the edges of a container that would indicate which direction from the current location they were. This required a lot of geometry and math that account for rotation and pitch and would have taken me some time to work through, but it was just a few seconds for the language model and it worked perfectly.

- I have some petunias that I haven't managed to kill and I heard that when a stem breaks off they can be replanted. I asked it how to do this and after warning me that selling these could constitute a black market, it helped me start several petunia plants that are thriving. My petunias are basically immortal now.

I empathize with the astroturfing concern, I file almost every statement released by Anthropic/OpenAI as bullshit. But they are an amazing tool given the right circumstances.

Writing code to spec is one thing, but creating art was always supposed to be what separated us from machines. (I suppose I need to preemptively acknowledge the "it was machine-generated so by definition cannot be art" point of view.)

But God, I could not understand the code, and I could not easily make it work with modern technologies (GPU etc).

So I used Claude and Gemini to reverse engineer the codebase, extract the core ideas, and rewrite it from scratch with modern frameworks (with guidance from the original authors)

It took me only 10 days to have a functioning equivalent, in 10K lines of code (using many libraries that did not exist in the 90s and 00s), which I find much easier to understand, even though I wrote none of it myself.

10 days to rewrite 20-30 year of a few persons. That was quite scary.

Reverse engineered an old audio recorder USB driver which only works in windows 7 and also reverse engineered the custom audio encoding the device uses and the software to convert it to a standard wav file. This took recording the USB traffic with Wireshark for each function in the original software in a VM then disassembling the various dlls and exes and driver files and feeding them into Clause step by step.

That AI button in DataDog not only diagnosed the problem across micro services but also created a fix PR. I think we might be unemployed soon.

Then it was models like Minerva that could actually solve math problems, and the discovery that LLMs were one-shot learners and could write code.

After that, the improvement felt pretty steady, with IMO gold feeling like a watershed moment.

And recently OpenAI's solution to the planar unit distance problem is starting to actually freak me out a bit.

Now it sits in a slack channel, and I watch it doing work, responding to ambiguity, and taking feedback/edits all day. It's unreal. It's literal magic. It saves a HUGE amount of time and gave us a pattern to do more.

This is the real deal. It's not easy to find problems with the right shape, and it's not easy to build agents that fit even when you do... but once it clicks, it clicks.

I now try to keep pushing the boundaries and see where it stops understanding my intention. Give it impossible tasks, gigantic projects, complex architectures. Last result: I wrote a complete OS including MPI, TCP/IP, and a GUI from scratch in only a week, while investing just a few hours a day in it. It even runs Doom!. Coding as a profession is over, but there's such a difference between the result if you approach this with a professional mindset, that I think the software engineering discipline can still provide massive value.

I wanted to see if I could build an image editor for isometric graphics using HTML5 canvas, Svelte, Vite, and the. Rather than do all of the skeleton code setup, I figured “why not try and see if Claude can build the app scaffolding?”.

I gave it a prompt and watched it produce the scaffold, along with a few features I outlined in the prompt.

When I booted the app and saw that the features worked and that there had been an element of design to the layout, that was my mind-blown moment. In a period of about 45 minutes, I added some features and had a basic MVP at the end. I walked back home stunned.

That app is available for free at https://babspixel.com

A milder version of it was Copilot setting up an environment for a Jupyter notebook. What would have been annoying back and forth between googling and docs went like a breeze.

Normal software instincts plus access to a different service flushed the buildup of OTA's and lives on as a fallback mechanism. Amazed me going from idea to execution faster than I could have ever dreamed of even on-boarding myself to the area or environment.

I still find it mandatory to write a lot of kinds of code by hand, but I write a lot of code with agents too now, and I previously literally didn't think that'd happen in <5yrs.