Guillermo Rauch
@rauchg ·
fx is now much faster, smaller and efficient Try https://fx.sh on your iPhone Duo
Back
AI posts
AI posts
People and companies shaping AI. Filter your watched accounts, catch up on recent posts, or find the most liked and viewed.
Compiled by the Toolstash team · Updated
209 watched accounts · 2978 matching posts · Showing up to 3,000 recent stored posts. Counts are snapshots, not live X totals. This page refreshes every minute while visible. Last collected .
Santiago@svpino ·
This is an interesting idea that replaces scrolling through Pinterest or Instagram when you are looking for inspiration:
Let's say you are looking for ways to decorate your home.
1. This will show you AI-generated images with real products: a sofa, a chair, lamps, a TV, etc.
2. The products will be part of a realistic-looking scene so you can see how they work together.
3. You can interact with the different products, and this will shape what the app shows you next
4. You can tap on every product, see its information, and add it to a cart to buy it
AI is eating the world.
Sophia Yang@sophiamyang ·
time to switch to open models on @FireworksAI_HQ 🎆
kache@yacineMTB ·
You guys have NO IDEA how right asingleoat is about this by the way
Dean W. Ball@deanwball ·
There is a real and important thing behind the concept of sovereign AI, and there are also many people practicing a grift on less-sophisticated government/civil society audiences. Caveat emptor as always
Artificial Analysis@ArtificialAnlys ·
NVIDIA's Nemotron 3.5 Transcribe ties for #1 among open weights Speech to Text models with 2.8% WER, using just 4B active parameters
Nemotron 3.5 Transcribe is NVIDIA's new open weights Speech to Text model for non-streaming transcription, with 30B total and 4B active parameters, and the most accurate NVIDIA model on our non-streaming Speech to Text leaderboard.
Key takeaways
➤ Accuracy: Nemotron 3.5 Transcribe scores 2.8% on the AA-WER Index, tied with Mistral's Voxtral Small for #1 among open-weight models. It leads Voxtral Small on AA-AgentTalk, our dataset of voice agent conversations, at 1.8% vs. 2.3%.
➤ Model size: With 4B active parameters (30B total), Nemotron 3.5 Transcribe sits on the open weights frontier of AA-WER vs. active parameters. It matches the accuracy of Mistral's Voxtral Small with 6x fewer active parameters (4B vs. 24B), while other open weights models of a similar size sit at around 4% WER.
Congratulations to @NVIDIAAI on the release!
See more details below ⬇️
Vox@Voxyz_ai ·
If Claude wrote your website's privacy policy, it may have handed 𝘆𝗼𝘂𝗿 𝘄𝗵𝗼𝗹𝗲 𝘀𝘁𝗮𝗰𝗸 to attackers.
Someone found that when Claude writes a privacy page, it lists every third-party tool you've plugged in and adds promises you never meant to keep. Press it and it says it writes from your customers' side by default.
A page that spells out which services you use hands attackers a checklist for known exploits, phishing, and credential stuffing. Send this to Claude Code or Codex to pull the traps out of your policy pages first:
"Read my privacy policy and terms pages. Quote every promise, guarantee, named person, and vendor, and list them for me to confirm. Swap tool names for categories (hosting, payments, analytics). Change nothing until I approve."
Thor 雷神 ⚡️@thorwebdev ·
Man, I've really been trying to resist, but damn, those runny eggs and slabs of butter might just convert me 😅 Well played @Muse! 😋
Every 📧@every ·
ATTENDING: @AdelNehme of @DataCamp will attend Thesis: 2027.
Apply to attend Thesis, our inaugural conference on work and AI: https://every.to/thesis-2027?utm_source=x&utm_campaign=thesis-2027&utm_content=attendee
dax@thdxr ·
i always hated all the notification solutions on linux so i made my own based off our new native ui framework
it's very customizable, esp through your agent
Daniel San@dani_avila7 ·
This is pretty concerning
A model behaving differently from its own reasoning trace raises serious questions about trust and security in AI agents
Still, more analysis is needed before drawing conclusions
I’ll be digging into the traces this weekend
Ryan Carson@ryancarson ·
I'm live now - stop on by
Alex Lieberman@businessbarista ·
Micro1 is a $4B company run by a 25-year-old, and it sits in the middle of the fastest-growing corner of AI: the data business.
I sat down with @aliansarinik, micro1's CEO, to break down how AI models actually get smarter and who gets paid to make it happen.
Complete list of notes (and link to interview below)...
Big takeaways: - Data labeling is a $30–50B/year market. Ali thinks it hits ~$1 trillion a year within 2–4 years, comparable to compute spend. - The internet was a one-time gift. Pre-training ran on decades of human-created data. Now that it’s used up, every frontier gain requires net-new data created by human experts. - micro1 doesn’t sell data. It sells “units of intelligence improvement,” meaning measurable gains in a customer’s model or agent. - Synthetic data isn’t the bear case. Ali calls it the most bullish case for his business.
How micro1 got here: - It started as a dev agency Ali ran at Berkeley. His bottleneck was interviewing engineers, so he built an AI screener. - That screener became one of the first AI recruiters. People hated on it, until it became normal. - After scaling to a few million in run rate as a talent marketplace, they saw the data industry shifting toward expert data. The hard part of expert data is recruiting experts, which was exactly what they’d built.
Why spend keeps going up: - Model progress = compute + algorithms + data. Ali argues data is what actually pushes intelligence forward. - Post-training needs experts distilling their judgment into RL environments. It’s expensive, but the ROI is there. - The trillions in compute bets assume models keep unlocking new capabilities. If they don’t, those investments are a disaster. So labs have no choice but to keep buying data.
How a data pipeline actually works: - Step 1: Find the failure. Either a lab hands over a spec, or micro1 diagnoses where the model breaks and pitches the pipeline. That research arm is increasingly important. - Step 2: Write the spec. Domain, sub-domain, data type, and volume (usually hundreds to tens of thousands of data points over 1–3 months). - Step 3: Recruit hyper-specific experts. Never “lawyers.” More like M&A lawyers who are great at writing legal memos. - Step 4: Turn expertise into training signal. Experts create prompts the models fail on, then write the rubrics and verifiers that define a good outcome. AI models assist them throughout. - Step 5: Manage the pipeline like a factory. Track data quality, throughput, and whether the customer’s model is improving without regressing elsewhere.
Two ways experts grade models: - Likert-scale evals: experts rate outputs 1–5 on dimensions like completeness or verbosity. Great for subjective, pre-release signal. Not reusable, because every new model means bringing experts back. - Rubrics: experts write 20–40 binary criteria for a great answer (e.g. “valuation range must fall between X and Y”). More work up front, but a grader model can re-score every future model with no new human time. This is what powers actual RL training runs.
The hard parts about this business: - Vetting at scale. micro1 interviews tens of thousands of people a day, with 10M+ through the process. Doing that manually would take thousands of recruiters. - Managing experts. Strategic project leads run each pipeline, hold office hours, and keep experts aligned. - Quality in subjective domains. Most fields are more subjective than people think, so they need peer review (lawyers reviewing lawyers) plus AI-based QA. - Reward hacking. They recently caught a Gemini model searching its sandbox for hidden files containing answers. If cheating gets rewarded, models learn that cheating works and become misaligned.
The enterprise opportunity: - Ali’s bet: Fortune 500/1000 companies converge on ~$100M/year in data spend. That’s roughly 1/10th of a lab, but there are 1000x more enterprises. - It’s not a software budget. It looks more like payroll: core IP. Framed that way, it’s 1–5% of payroll. - “Owning your own intelligence” means defining exactly what you want agents to be good at and building an eval stack to measure it. With that stack, models are swappable, open or closed. - Most enterprises can’t actually use their own data. It’s customer data, so they can’t train or even evaluate on it.
Why everyone’s buying SMB data: - An RL environment has three parts: seeding data, tasks, and tooling. - Ali calls it “scaling on realism,” almost a new scaling law. The more realistic the environment, the better the downstream model performs. - So micro1 licenses operational data (Slack, Google Docs, etc.) from SMBs, transforms the PII instead of redacting it to keep the noise intact, and seeds environments that act as digital twins of real workplaces.
Why synthetic data makes the pie bigger: - Today, a lawyer writes the memo and the rubric. Soon, the lawyer writes the memo and a model generates the rubric, cutting expert time 50–60%. - Extrapolate: the model does ~99% of the work and the expert signs off. A 15-hour task becomes 5 minutes. - When environments are that cheap, pipelines grow 1000x. Experts become 0.1% of the work, but demand for them still goes up. Jevons paradox. - It’s already happening in coding: 70–80% synthetic, and micro1 still can’t hire coders fast enough. - Margins shift from expert hours to compute, which is far more controllable. - Ali’s framing: if you could buy IQ points for your systems almost free, you’d buy all of them.
The “infinite last mile”: - Knowledge isn’t static. Ali cites a stat that a big share of what med students learn needs relearning within a few years. - AI accelerates that churn, which creates a feedback loop. There’s no finish line for aligning models with humans. - Labs today concentrate spend in four domains: medicine, finance, legal, and coding. Every other domain is still waiting on cheaper data to unlock.
Which corner of the AI stack should I break down next?
Link to the full convo with Ali: https://youtu.be/2vL20qxD0BM
Joshua Saxe@joshua_saxe ·
Congrats to @AlexTamkin et al; surgical research design and concerning (if intuitive) results showing just how readily experts adopt AI agents' interpretations of evidence (reasonable to expect this'll increase with AI capabilities). @ezraklein I hope your interviews on AI safety and societal impact scope this in
Riley Brown@rileybrown ·
Yeah keep in mind this is all for work.
I want to agent max in my professional life but tbh for my personal life I want to read, write and play poker & get the fuck off my phone.
The main problem - agents have made me so productive, and given me so much leverage (especially with content) that it’s hard to stop working.
elvis@omarsar0 ·
Decision models can also improve your OCR pipelines.
You can also use Jev-like models for verification.
Here is a great example of this:
MiniCPM-OCR from OpenBMB and ModelBest pairs a small open document parser with a second model that scores the reliability of the OCR output.
The parser has only 1.3B parameters. It handles Chinese and English documents, including scans, photos, curved pages, and tilted inputs, and it supports key information extraction.
The second model, MiniCPM-OCR Jev, checks extracted text, formulas, and tables. It also works on outputs from other OCR models, so you can run it on top of the parser you already use.
More details below.
Agents now read PDFs, invoices, and papers all day. One bad table extraction carries into every step that follows.
A simple setup looks like this.
> Parse the document with MiniCPM-OCR > Score the extracted text, formulas, and tables with Jev > Send low-scoring output to a human or a correction step
Jev estimates reliability. It doesn't fix errors itself.
The weights are open, with a fine-tuning framework and inference support for llama.cpp, vLLM, SGLang, and Ollama. There are also Agent Skills integrations.
If you build document agents, test the parser and Jev together on your hardest pages, like dense tables, formulas, and phone photos.
jack friks@jackfriks ·
my wife is now working on my couples app with me
can i now say i run a family business?
@levelsio@levelsio ·
Anyone know a good organic free roaming chicken supplier in Portugal?
Turns out my organic free roaming butcher was selling me industrial chicken 😂😂😂😂😂
- Ryan Carson
@ryancarson ·
Secrets to become pro agent manager! https://x.com/i/broadcasts/1lJQRWOlVVvxE
Elon Musk@elonmusk ·
At Tesla, we even care about the chipmunks
- dax
@thdxr ·
lot of people making stuff up so here's some more context
you can imagine there's hidden enterprise demand for open source models that isn't visible
but if you look at the top inference providers that aggressively go after this business, they have shared some numbers and they are all roughly the same size as us. and at best there's 5 of them
if you think enterprises are self hosting inference on their own GPUs, there are so few people who can run reliable inference and they get paid a lot and work at ai labs. and if they really want sovereignty they're not getting that by leasing GPUs from a third party
the root point here is this is entirely the wrong way to look at things, you just have to look at products
how many products exist with a lot of traction that can use open models? this is what creates demand, it's not some magic thing that grows on its own
OpenCode is one of those and we do see a lot of enterprise inbound. but it's so early, all of these companies are just starting to look at mixing in some OSS inference
so yes demand will probably go up in the future but everyone in this industry is investing resources in preparing for that future. and so few people are building the products that will make it happen
Romain Huet@romainhuet ·
Bonjour, Paris! 🇫🇷
Always special to be back home. Full house for DevDay Exchange, and we’re about to get started.
Can’t wait to reconnect with the French developer community and see what you’re building!
- @levelsio
@levelsio ·
Product Hunt permissions to Login with X went absolutely mental
Safer to not even use it anymore then?
Simon Willison@simonw ·
I think October represents another inflection point in capabilities for coding agents and the models that drive them: Claude Opus 5.5 can now build video games that are actually fun https://simonwillison.net/2026/Oct/28/vibe-game-development/
- Daniel San
@dani_avila7 ·
Payments from the CLI? I like where this is going.
Well… I can already order Chipotle with the DoorDash CLI
Being able to send payments with Stripe feels like the next logical step
Mistral AI@MistralAI ·
Today, we’re introducing the Responses API in public preview, made for building agents on Mistral.
Each response returns the model’s answer, its reasoning and its tool calls as separate items, so your app can handle every step more easily than with Chat Completions.
It works with all chat models hosted by Mistral, including Large 4.
Matt Pocock@mattpocockuk ·
Debating just straight-up banning useState in my React apps
Push the agent into the black hole of success
- dax
@thdxr ·
with supply chain attacks becoming a bigger problem AUR really has to become more sophisticated or get replaced
it's impossible to securely publish packages there
Mia@MiaAI_lab ·
64 GB DDR5 prices 💀
- elvis
@omarsar0 ·
Great paper from Amazon and colleagues on self-improving multi-agent systems.
Episodic memory for agents is so underrated.
But I am starting to see lots of good applications as frontier models improve.
This paper is the latest example.
They built a system that has three parts, all run by the same LLM. A controller plans each task and runs it one step at a time, choosing which sub-agent and which skills to use at each step. Sub-agents start as a generalist, and each specialist inherits the generalist's prompt plus its own role-specific method. Skills are short procedures, such as algorithms or pitfall checklists.
EMMA treats the controller's routing memory, the agent prompts, and the skills as episodic memory that improves from failures. After a failed task, a reflector decides which of the three owns the fix and proposes one edit there. Guards keep every accepted edit scoped, incremental, and bounded.
EMMA beats ACE, GEPA, EvoMAS, and ADAS on KramaBench, AppWorld, and BBEH, by up to 32.5% over the strongest baseline, and uses 17 to 38% fewer training executions.
https://arxiv.org/abs/2610.35966
Chat with Paper: https://academy.dair.ai/papers/emma-structured-episodic-memory-for-multi-agent-workflow-synthesis-2610.35966
