Do you believe in the second brain idea? I mean a system where you collect your notes and your reading in one place and then think on top of it with AI. Does anyone here really use one, or does it stay a nice hobby?
I built one for myself and called it Brainless. It is not an app. It is a folder of plain Markdown notes with a set of scripts around it. I capture everything: voice notes and photos from my phone, meeting transcripts, books and articles. Every night the scripts compile this into a wiki. They write summaries, link related notes and check for contradictions.
Then I work on top of it with Claude. When I ask a question, it searches my own notes first and shows me where each claim comes from. If my notes have nothing, it says so.
When I need to go deeper, it runs a research round and the result goes back into the wiki, so the next question starts from a better place. It is open source: github.com/ezapmar/brainless-public
I built it for four reasons:
My notes were everywhere. I needed to collect them and go deeper.
I want to think in a sharper way. For that you need knowledge, and it has to live somewhere.
I have to make decisions. They are weaker when I cannot see what I thought before and where I was wrong.
I need an assistant for daily work: setting up user interviews, preparing answers for the press, first drafts of blog posts.
I never give it the main job. I decide and I write. It prepares and supports. I call this "AI for earthlings". Everybody talks about armies of agents. I find it more interesting when AI simply makes a normal manager's day easier.
How do you use it? If you have a trick that works, I would love to hear it. And if you tried and gave up, I am curious why.
Miles Brundage says he regrets advocating for iterative deployment in the GPT-3 era, arguing that the risk picture has changed as AI capabilities and real-world harms have grown.
That raises a hard question: how should deployment decisions change as the stakes rise? What evidence should make a lab slow down, and who should get to decide?
Really interesting framing from Alex Zhang on the “shape” of language models:
We’ve spent the last few years building increasingly sophisticated harnesses around a mostly fixed primitive: models that take in and put out arbitrary text.
As a result, we've mostly treated agents, tool use, memory, compaction, context management, and planning as problems for the harness to solve.
Alex explores what it might look like to invert that: instead of always designing the harness around the model, what if we designed models around the harness / the tasks to be completed?
Jev is an example of this — rather than generating arbitrary text, its output is constrained to a value between 0 and 1, making it dramatically more specialized, and also enabling a different computational tradeoff (very fast fuzzy decisions that can become primitives inside larger agent systems).
The implication is that the future may not be one increasingly intelligent general-purpose model sitting inside increasingly complicated agent harnesses. It may instead be systems composed of models with different “shapes,” each optimized for a particular role.
Once frontier capabilities can be distilled into smaller/open models, this design space becomes much more practical to explore.
So maybe the question worth asking now is: what should models look like if we designed them specifically for agents?
turbopuffer is moving vector search from the primary index to just another secondary index in v3. Coming from a company that launched as a vector database, that's a strong signal on where search infrastructure is heading.
Real-time transcription in 60 languages with first words in roughly 100 ms and about a 2.5% word error rate, alongside two new voice models. Microsoft's in-house model lineup keeps filling out.
Chinese labs have embraced open weights more than almost anyone, and I haven't heard an explanation that fully satisfies me. There are three common ones.
𝗖𝗼𝗺𝗺𝗼𝗱𝗶𝘁𝗶𝘇𝗲 𝘁𝗵𝗲 𝗰𝗼𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁. This is why open models make sense for chipmakers: better free models sell more GPUs. China's chipmakers aren't there yet. The closer fit is cloud: Chinese hyperscalers sell compute, and open models bring customers to it. But that doesn't explain independent chinese labs, which have no cloud business to feed and have done as much as anyone to drive the trend.
𝗘𝗱𝗶𝘁𝗼𝗿𝗶𝗮𝗹 𝗰𝗼𝗻𝘁𝗿𝗼𝗹. The theory is that open models spread Chinese framing on sensitive topics. But open weights are the weakest way to control outputs, since anyone can fine-tune the censorship out, and people have. A closed API would give far more control.
𝗔 𝗹𝗼𝘀𝘀 𝗹𝗲𝗮𝗱𝗲𝗿 𝘁𝗼 𝘂𝗻𝗱𝗲𝗿𝗰𝘂𝘁 𝗪𝗲𝘀𝘁𝗲𝗿𝗻 𝗹𝗮𝗯𝘀. Loss leaders get recouped through lock-in later, and open weights create almost none. This also assumes a coordinated plan among companies that compete hard with each other.
My bigger issue is that each of these explains a motive, not a strategy. A well-considered strategy should tell you what winning looks like, and why it still pays off if the future unfolds differently than expected.
So I'd love to see someone walk through how open weights play out under a few different futures:
𝗦𝗰𝗲𝗻𝗮𝗿𝗶𝗼 𝟭: 𝗠𝗼𝗱𝗲𝗹𝘀 𝗯𝗲𝗰𝗼𝗺𝗲 𝗰𝗵𝗲𝗮𝗽 𝗰𝗼𝗺𝗺𝗼𝗱𝗶𝘁𝗶𝗲𝘀. Capability converges and the value moves to applications, deployment, and hardware.
𝗦𝗰𝗲𝗻𝗮𝗿𝗶𝗼 𝟮: 𝗧𝗵𝗲 𝗳𝗿𝗼𝗻𝘁𝗶𝗲𝗿 𝗸𝗲𝗲𝗽𝘀 𝗰𝗼𝗺𝗽𝗼𝘂𝗻𝗱𝗶𝗻𝗴. Whoever leads stays ahead, and the best model is worth far more than the second-best.
𝗦𝗰𝗲𝗻𝗮𝗿𝗶𝗼 𝟯: 𝗧𝗵𝗲 𝘄𝗼𝗿𝗹𝗱 𝘀𝗽𝗹𝗶𝘁𝘀 𝗶𝗻𝘁𝗼 𝘀𝗲𝗽𝗮𝗿𝗮𝘁𝗲 𝗔𝗜 𝗲𝗰𝗼𝘀𝘆𝘀𝘁𝗲𝗺𝘀. The question becomes whose stack everyone else builds on.
What does China get in each, and how would they adjust if the future they're betting on doesn't arrive?
My assumption is that there is a more defined strategy that accounts for these, but I have not seen much published on this. Has anyone seen a compelling explanation?
Google's 10-year update pledge runs into a mid-2034 ChromeOS cutoff, so anything launched after mid-2024 comes up a couple years shy. Their answer is a migration path to Googlebook OS for qualifying devices
The OpenStreetMap app that turns mapping your block into little quests (street names, surfaces, opening hours) is out of Android-only after years of porting. Public beta on TestFlight now. Love this one
The minimal coding agent everyone on HN loves is officially stable -- works with every major model provider, now has MCP, plus an experimental Pi Durable for long-running jobs. Same team that said no MCP a year ago
I’m using agents for more and more of my work, and it’s very easy to end up with a bunch of things running at once. I’m less convinced I’m good at keeping up with all of them.
One needs a decision, another says it’s finished but I need to check what it actually did, and then there’s a chat I haven’t looked at for a while and can’t remember where we left things.
They’re still saving me a lot of time. But I sometimes get to the end of the day having spent most of it moving between chats, reviewing things and giving the next instruction. There’s always something else I could set going.
For people doing this regularly, how are you managing it? Have you found a way to keep more work running without spending the whole day keeping track of it?
There's (understandably) a lot of concern about the negative environmental impacts from AI, but I don't see much conversation about what it's unlocking for clean energy.
Without the massive and price ineslatic demand for elctricty from the AI build out, there wouldn't be the same momentum or pathway for clean energy sources like nuclear.
So if over the next few decades nuclear (and things like next gen gas turbines) become a significant, or even dominant, source of US power, then the net impact on the environment from AI might actally be positive.
Months ago, Elena Verna shared this idea that IC work is the new career flex. I have been thinking about what this means for marketing leadership. The marketer who still loves the work and leads from the trenches. The chief marketer, instead of the chief marketing officer.
"The harness is now what shapes what must be true for work to ship (~trust), how fast and how the products land (~distribution), the speed and context of feedback loops (~efficacy), and how institutional knowledge is ingested and maintained (~domain context).
Harnesses go from internal tooling you’d happily buy to something you’d no more outsource than your product-eng org or your GTM team. This is distinctly different from the pre-AI world, where outputs were mostly bounded by the humans using the software to get things done."
Diogo Almeida says they're serving a trillion tokens per day as of last week. The model is just 3 weeks old and is already spawning copycats.
Curious to hear if anyone here is using Jev in production / what you're using it for?
I’m curious if people think transformers will be the architecture AGI is born out of.
People seem to fall into 3 buckets:
1. Scaling the existing recipe is enough and more parameters, data and compute will get us there. Scaling laws predict improvements, but are they enough for general intelligence?
2. The architecture is sufficient, but the learning recipe needs to evolve (more RL, interaction with environments, more inference time compute, multimodal learning cross text, images, video and audio).
3. We need a totally different architecture or representation of the world (e.g. changes to memory, learning mechanisms, or tokenizing in 4D/3D vs 2D might be needed for spatial intelligence).
What do folks here think?
In my customer discovery interviews for OpenWay AI, I’ve often asked what people expect AI to do for their business.
One question I keep coming back to: are we using AI to help knowledge move through a company, or adding more places for it to get lost?
We’d expect AI to help. Yet every summary and handoff creates another point where someone, or something, decides which details survive.
Imagine a buyer finally says yes after sales explains the rollout. AI records “implementation discussed.” Marketing uses another AI tool to draft the next campaign from an existing brief, which still assumes price is the main objection..
We already had to manage what gets lost between people. Now we also have to manage what gets lost when AI compresses a conversation, decides what matters, or works from an incomplete brief.
When you evaluate AI at work, do you check what gets lost between the outputs?
Imagine AGI is achieved and alignment is solved. AI can now do almost everything. Only a handful of tasks still require humans, and very few people get to do them.
What does everyone else do?
Maybe you can play games, travel, climb mountains, or spend time on hobbies. But none of it is needed. Whether you succeed or fail changes very little.
For most of history, people have had something to work toward because their effort mattered. They could build something, discover something, solve a problem, or become useful to others.
What happens when there is almost nothing left for humans to contribute?
What do you strive for when nothing important depends on you anymore?
Anyone have advice on how to build multi-step, agentic workflows using Claude Code or Codex?
I'm a non-dev and I find coding harnesses great at building any single slice of a given piece of software but generally less good at building multi-step workflows where a piece of work output is created that then needs to be acted on by an agent or another part of the system and then handed off to the next agent or next part of the system.
Hopefully that makes sense.
From the blog:
"AI chips shipped through 2027 could run tens to hundreds of millions of concurrent frontier-model agents. Running nonstop, these agents would supply as many weekly working hours as about 140–720 million full-time employees.
More efficient models could potentially support billions of agents on the same hardware. Applying DeepSeek V4 Pro serving benchmarks to the projected hardware supply yields approximately 1.9 billion concurrent agents supplying as many weekly working hours as 8 billion people each working 40 hours."
Anthropic set a September 30th deadline, but we've yet to see any announcement of their provable inference prototype. I thought this was interesting because it's a very important part of the AI supply chain that folks aren't tracking, and it could establish responsible training standards.
> We will develop a prototype by September 30, 2026 of provable inference, a technique for reliably, provably “signing” AI model outputs in a way that makes them attributable to a specific set of model weights. In the future, it’s possible that very sophisticated attackers will seek to infiltrate our systems and modify our models after we’ve trained them - whether to sabotage our work or co-opt our models into serving their own goals. If we could reliably and systematically verify that model outputs were coming from a specific set of model weights, we believe this threat would be significantly reduced.
as i watch engineers fall deeper into the belief that an amalgamation of mathematical probabilities somehow understands their codebase better than they do, i find myself thinking back to a time when software wasn’t built for hypergrowth, but simply to do x without inventing y.
ai seems almost fundamentally opposed to this philosophy. ask it to do x and it will eagerly invent y and z before it has even tried to understand x.
the danger isn’t that ai writes worse code- it’s that it makes writing unnecessary code 100% free
and the human condition is such that some of us will always prefer the fast, steep gains of ai, even when it does a bajillion unrelated things to accomplish something that could have been done without changing anything else.
so, somewhat paradoxically, the quality of software may keep declining for as long as ai keeps getting better. the cheaper complexity becomes to create, the less incentive there is to understand or avoid it.
Going to drop more work of ours on this platform. We secured openai by finding a complex exploit chain.
Two bugs let us take over ChatGPT/Codex accounts of OpenAI employees (+some unaffiliated users) and reach connected services: Outlook, Slack, GitHub, etc.
We reported and OpenAI fixed it in 14 hours. We will be publishing similar work soon.
We found asteroids, nebula, and even the satellites I helped build and launch at Astranis. We also technically found a UFO, but it was probably another satellite
Gallup reports that 31% of Americans consider college “very important,” down from 70% in 2013, but it doesn’t tell us whether a degree pays off for a particular person or field.
Jeffrey Tucker’s take is that college can amount to a long, subsidized detour for people who don’t face demanding post-college credentialing, and while I think that raises a useful question, it risks treating very different paths as one thing: some careers require a degree and further training while for others, the cost and time may be hard to justify.
How should we judge college’s value? By career requirements? Earnings? Learning? Something else?
I'm excited about HAA as an alternative: https://www.theacademysf.com/
I receive an email or ad about once a week trying to get me to sell my company's data to Micro1, Handshake, Mercor, or some other middleman. I also get the same emails about my portfolio of startups. The pitch is always for millions of dollars of potential value.
I am curious if any founders have actually sold their company data and what the process has been like. Would you recommend this for struggling companies that need resources or a broader set of businesses?
Hi everyone,
I'm Abinash. For the past few weeks, I have been working on building a neural network in Rust to test the idea of using Rust in model development and research.
Rust is being extensively used in LLM infrastructure and is growing in the inference space as we get official libraries to run CUDA kernels from Rust binaries.
But if you see the model design, research or development space, Rust is non-existent.
So I thought I'd give it a try. I tried to build a fairly easy neural network to solve the MNIST handwritten digit identification problem.
I know it's not a huge milestone. But I tried to give it a try to test my idea how easy it is to develop neural networks in Rust.
The neural network I'm developing has 2 hidden layers with 512 and 128 neurons, respectively. The goal is to achieve 95% accuracy on my local CPU-only system.
I got the input system right; it can now take training and testing datasets and prepare the matrices for the training and testing stages.
After that, I plan to start developing a real transformer-based LLM from scratch in Rust.
I'm not sure how it will play out, but I will give it a try.
I'd love to have your thoughts on it. If you're a senior ML engineer or MTS at a frontier lab, I'd love to have your thoughts on it.
As AI makes execution increasingly cheap, the ability to properly steer and verify AI outputs better than your competitors will be the differentiator. World-class verification factories rely on two assets: unique ground truth and talent.
Anyone felt that progression yet?
-You make 10 products with astra and a couple API keys.
- You're euphoric about 2-3 of the products.
- You then realize once you put it out there anyone can also replicate it with astra.
You have a number of feelings in between that somehow conclude in;
You decide you need to instead build a robot in your garage.
I increasingly don't care about the 2-point benchmark gap between frontier models. I care about their failure shape.
Two models can both score 90% and feel completely different to build on.
One gets something wrong and you catch it immediately.
Another makes one incorrect assumption early, spends the next 20 steps building on top of it, and gives you something coherent enough that it passes a quick skim. Same score but very different effectiveness.
This matters a lot more as we give models longer-running tasks.
Does the model notice when it's off track? Does the mistake stay contained? Can it recover? And how expensive is it for me to figure out that something went wrong?
Benchmark averages still tell us something. But at this point I want to know what the remaining 10% actually looks like.
They're starting w open post-training recipes and planning open infrastructure research into recursive self-improvement, reward hacking, and multi-agent systems.
(inb4 this converts to for profit when successful?)
i've been using wispr flow mostly but have also trialed aqua / superwhisper / others
curious if anyone has a strong opinion as to why they like one or another?
While we all are waiting four next Arma, here is a game Rio Grande 3D that spiritually started the journey for me, was attempted as first Bohemia game before Flashpoint and Arma, my brother and Bohemia Interactive in the 80s: River Raid.
Let me present you a game I wished we could make bacl then but had no idea about he power of mathematics. Rio Grande 3D for Atari 800XL.
Alex Duffy at Good Start Labs I think is still flying under the radar but is a remarkable talent. He lives at the intersection of AI and games. This is livestream on Twitch so sick.
Crew-13 to the ISS, 130 sats on Transporter-18 (Google's Suncatcher TPU sat was on it), then Falcon Heavy's first ever NRO launch to close it out. One booster was on its 25th flight. Reusable rockets are just normal now
A lot of tech advances take something that used to be a luxury and give it to everyone.
Clothes cleaned for you used to mean paying someone to come scrub them. Washing machines.
A personal driver ready for you at any hour of the day was very expensive. Uber.
Instinct and Meta’s Muse are doing the same thing for personal assistants.
The question is, do most of us not have a personal assistant because it’s too expensive, or because we just don’t need one?
If you only have the occasional trip or boring online task, you probably won’t stay in the habit of using one.
But now that the price is zero, maybe we’ll find a lot more use cases than we think?
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