I'm building a microreading service that let's me get long books read with small chunks of time that I have - https://lauselt.ee Currently I've added some public domain Estonian books in there and tbh I do get a lot more reading done during the day. Basically you can use your 1-5 min breaks (waiting for a bus, during the commercials, waiting for food etc) to open the book quickly where you left off and read by scrolling small chunks of texts at a time. Duolingo style streak to create the habit of reading every day.
Also the ability to upload your own book and it will automatically be split into these small chunks.
Since subreddits related to identifying AI images/videos got very popular, my wife started to send me cute AI generated videos, older family members can't distinguish AI videos at all, I've decided to code a weekend side project to train their Spidey sense for AI content.
Well, I just jumped full time on IronCalc[1] a fully open source, light and fast spreadsheet engine designed and build from the ground up.
I have been working on it as side project for over two years and now, with funding from the EU for the next 2.5 years, I hope I can make of it a real product for everyone to use that can compete with the likes of Excel and Googl;e Sheets.
Cool. I just learned of compass and straight edge calculations from this video on doubling a cube https://www.youtube.com/watch?v=96LbF8nn05c from
Ben Syversen's channel a couple of months ago
> "..am I alone in finding the expression 'it turns out' to be incredibly useful? It allows you to make swift, succinct, and authoritative connections between otherwise randomly unconnected statements without the trouble of explaining what your source or authority actually is. It's great. It's hugely better than its predecessors 'I read somewhere that...' or the craven 'they say that...' because it suggests not only that whatever flimsy bit of urban mythology you are passing on is actually based on brand new, ground breaking research, but that it's research in which you yourself were intimately involved. But again, with no actual authority anywhere in sight."
The Mass Effect universe distinguishes between AI, which is smart enough to be a person—like EDI or the geth—and VI (virtual intelligence), which is more or less a chatbot interface to some data system. So if you encounter a directory on the Citadel, say, and it projects a hologram of a human or asari that you can ask questions about where to go, that would be VI. You don't need to worry about its feelings, because while it understands you in natural language, it's not really sentient or thinking.
What we have today in the form of LLMs would be a VI under Mass Effect's rules, and not a very good one.
Don't believe the hype, this is tribal thinking. Everybody seems to have these widely diverging opinions on AI lately. What does AGI have to do with predicting the next token stochastically like a parrot? Oh, people say you can brute-force AGI, if only things are answered correctly. I get that, I still see SOTA models sometimes fail like babies. I also mostly see them perform at a much higher intelligence and work ethics than I can, but maybe I'm too hard on myself.
Anyway, here's something I've recently build that shows the HN consensus when it comes to AI-Coding (spoiler: they say it's quite good): https://is-ai-good-yet.com/ Is AI “good” yet? – A survey website that analyzes Hacker News sentiment toward AI coding.
Nice post :). It made me think of ugit: DIY Git in Python [1] which is still by far my favorite of this kind of posts. It really goes deep into Git internals while managing to stay easy to follow along the way.
From the title, I assumed this is about an app distributed as a binary blob.
Regarding learning languages, I'm not a fan of this style of learning. It seems to me this is still Duolingo, just with a different interface. I had good success with https://www.languagetransfer.org/
Claude is really good at specific analysis, but really terrible at open-ended problems.
"Hey claude, I get this error message: <X>", and it'll often find the root cause quicker than I could.
"Hey claude, anything I could do to improve Y?", and it'll struggle beyond the basics that a linter might suggest.
It suggested enthusiastically a library for <work domain> and it was all "Recommended" about it, but when I pointed out that the library had been considered and rejected because <issue>, it understood and wrote up why that library suffered from that issue and why it was therefore unsuitable.
There's a significant blind-spot in current LLMs related to blue-sky thinking and creative problem solving. It can do structured problems very well, and it can transform unstructured data very well, but it can't deal with unstructured problems very well.
That may well change, so I don't want to embed that thought too deeply into my own priors, because the LLM space seems to evolve rapidly. I wouldn't want to find myself blind to the progress because I write it off from a class of problems.
But right now, the best way to help an LLM is have a deep understanding of the problem domain yourself, and just leverage it to do the grunt-work that you'd find boring.
It still failed my image identification test ([a photoshopped picture of a dog with 5 legs]...please count the legs) that so far every other model has failed agonizingly, even failing when I tell them they are failing, and they tend to fight back at me.
Gemini 3 however, while still failing, at least recognized the 5th leg, but thought the dog was...well endowed. The 5th leg however is clearly a leg, despite being where you would expect the dogs member to be. I'll give it half credit for at least recognizing that there was something there.
Still though, there is a lot of work that needs to be done on getting these models to properly "see" images.
I think this article is pretty spot on — it articulates something I’ve come to appreciate about LLM-assisted coding over the past few months.
I started out very sceptical. When Claude Code landed, I got completely seduced — borderline addicted, slot machine-style — by what initially felt like a superpower. Then I actually read the code. It was shockingly bad. I swung back hard to my earlier scepticism, probably even more entrenched than before.
Then something shifted. I started experimenting. I stopped giving it orders and began using it more like a virtual rubber duck. That made a huge difference.
It’s still absolute rubbish if you just let it run wild, which is why I think “vibe coding” is basically just “vibe debt” — because it just doesn’t do what most (possibly uninformed) people think it does.
But if you treat it as a collaborator — more like an idiot savant with a massive brain but no instinct or nous — or better yet, as a mech suit [0] that needs firm control — then something interesting happens.
I’m now at a point where working with Claude Code is not just productive, it actually produces pretty good code, with the right guidance. I’ve got tests, lots of them. I’ve also developed a way of getting Claude to document intent as we go, which helps me, any future human reader, and, crucially, the model itself when revisiting old code.
What fascinates me is how negative these comments are — how many people seem closed off to the possibility that this could be a net positive for software engineers rather than some kind of doomsday.
Did Photoshop kill graphic artists? Did film kill theatre? Not really. Things changed, sure. Was it “better”? There’s no counterfactual, so who knows? But change was inevitable.
What’s clear is this tech is here now, and complaining about it feels a bit like mourning the loss of punch cards when terminals showed up.
https://gist.github.com/kemitchell/fdc179d60dc88f0c9b76e5d38...