Guillermo Rauch
@rauchg ·
Vercelnetes or V8S for short
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jack friks@jackfriks ·
im so proud of this app that i made :')
Mike Taylor@hammer_mt ·
Claude Sonnet 3.5 was the best model in the world 24 months ago – surely it's not going to take this long.
Mia@MiaAI_lab ·
👀
jason@jxnlco ·
Do you have a Yubikey?
- Mike Taylor
@hammer_mt ·
Claude has gotten good enough that you can build legit professional software for ~$20k in tokens, unfortunately the TAM for these products has also shrunk to $20k.
Riley Brown@rileybrown ·
Within 12 months both meeting notes and voice typing will be completely free.
- Guillermo Rauch
@rauchg ·
Enjoy!
- Riley Brown
@rileybrown ·
Not necessary, waste of focus. You can set up a gpt-image-2 skill on Claude in 20 seconds.
lauren@poteto ·
we will need to rebuild and rewrite much of software to adapt to a new, faster pace.
code used to arrive one drop at a time. soon, if not already, code will be written as if it were a continuous stream, like a violent river twisting and turning as it pleases.
this is not a new thing: big tech cos have had to deal with this with the enormous scale of their engineering teams. but now ai has brought an incredibly elastic and capable workforce wielded by every engineer. this is your problem now, and the only way to adapt is to evolve your systems so agents - even and especially the ones with little context about the right way of doing things - can thrive. make the easy thing the right thing.
your codebase is becoming organic architecture. agent contributions grow around the constraints and guardrails you’ve designed. good software engineering matters even more now! and no, the continuous stream of code is not something you have to watch helplessly. be more ambitious and design systems, frameworks, and languages to control the chaos.
the time for mourning coding is long over
Thor 雷神 ⚡️@thorwebdev ·
Hey New York 🗽 It's time to push the boundaries of what's possible with Gemini 4 💎 @cerebral_valley and @GoogleDeepMind are teaming up to bring you:
🏆 $20,000 in prizes 📍 In-person in New York City 👥 Teams up to 4
Space is strictly limited. Apply now 👇
Artificial Analysis@ArtificialAnlys ·
We handed GPT-6.1 Sol a robot arm and a pen, and it wrote the three scariest words in AI
Happy Halloween from AA-Robotics! 🎃
Elon Musk@elonmusk ·
Tesla Model Y long wheel base variant
Arena.ai@arena ·
Your agent would cost 260% more without the cache.
We analyzed +26K Agent Arena sessions from September 12 to October 23, 2026, spanning 30 agents and 13 labs.
Caching dramatically changes the economics of a typical task. If every cached token were instead billed at the model's full input price, a task sent to one of Agent Arena's top-10 agents would cost 3.6x as much (260% more).
The savings become especially tangible for leading models. A typical Claude Fable 5.1 task costs $7.44 today; without caching, it would cost $27.
Even a coffee break can carry a hidden price: if your cache expires while you’re away, the agent may have to resend the entire conversation at full input price. In our Agent Arena sample, a single cache invalidation translated to a 5% higher bill for a typical task.
Read more findings from our analysis of the hidden AI price war in the article below.
OpenAI@OpenAI ·
Privacy should advance as fast as intelligence. OpenAI Private Intelligence gives businesses stronger controls over their data while using our most capable models.
We’re building these protections in close collaboration with some of our largest customers to help businesses apply frontier AI to their most sensitive work. https://openai.com/business/private-intelligence/
Aravind Srinivas@AravSrinivas ·
Perplexity Computer can now control your Google Chrome browser from the Perplexity Mac and Windows app. A new chrome extension below to set it up once and you can just ask your agent to do any local browsing tasks on your Chrome browser
https://chromewebstore.google.com/detail/perplexity/pninphncmkphpjgfffammoenglghcjii
- Elon Musk
@elonmusk ·
This is literally their plan and they say it out loud!
Mitchell Hashimoto@mitchellh ·
Termius on iOS and Android now powered by libghostty. The most popular (or close to) mobile terminal! Looks great.
Igor Babuschkin@ibab ·
The River API now supports training LLMs with any loss function. You define a custom loss in PyTorch on your laptop and we efficiently execute it at scale on our remote GPU cluster with zero overhead.
The blog explains how it works: the loss function is traced on your machine, sent to the API as a graph, and the computation gets parallelized and streamed for memory efficiency.
Carmine Paolino@paolino ·
I’ve wanted to build Solco ever since I started DJing in 2012.
Managing a library of tens of thousands of tracks always felt tedious. Tagging. Fixing beatgrids. Using colours and ratings to remember energy and mood. Dragging tracks into the right playlist among hundreds you already have.
That’s backwards. I wanted to spend that time rediscovering my music and building better sets.
In 2013, that frustration became my master’s thesis: music genre recognition using deep learning. It could recognise 10 genres!
Today, Solco brings together a model covering 400 musical styles with models for mood, energy, key detection and beatgridding.
I built it as a native app, using the GPU for rendering and model inference, with fast disk access and acoustic fingerprints to find duplicates.
There’s MusicBrainz and Discogs tagging, Smart Playlists, and a playlist assistant built with RubyLLM to help you find the set hiding in your library.
It also writes USB drives for compatible Pioneer CDJs and XDJs directly.
In my first-export benchmark, Solco took 12 minutes 18 seconds. rekordbox took 3½ hours. Same 493 already-analysed tracks, Mac mini M4 and 250 GB SanDisk USB stick (FAT32). Median of three runs, against rekordbox 7.2.6.
Here’s how I measured it: https://getsolco.com/measured.html
Solco is free during the private beta. Join the waitlist and try it with your own music: https://getsolco.com
- Arena.ai
@arena ·
Ethan Mollick@emollick ·
I was interviewed in Vox about my new book, and a question on the meaning of work let me discuss something I have been thinking a lot about, comparing the logic of industrialization to the logic of craft: "You’re a hand-weaver in a world of power looms." https://www.vox.com/technology/508032/ai-jobs-ethan-mollick-co-existence
Peter Yang@petergyang ·
Attended a fun session at a16z with @illscience and @joshelman yesterday. As usual they were dropping knowledge bombs:
1. There are three economic miracles. The old one is zero marginal cost software distribution. But recently there have been two more: 1) Coding going from the most precious asset in the world to one of the cheapest and 2) zero marginal cost digital labor with agents.
2. It's hard to appreciate what it feels like to be on the exponential. Just 10 to 20 million people account for $150 to 200 billion of token spend. "Some of the best companies that we work with have higher token spend than payroll." We are still early.
3. The AI workspace, like ChatGPT and the Claude desktop app, is going to be the container for all kinds of AI interactions and it's going to subsume the browser. Personally, these apps are already the browser for me. Chrome is just the middle layer.
4. The way to tell if you're an AI-native company is to ask: If the model gets twice as good, do your customers get twice as much value? Are you happy or sad that the model got better? If you're sad, you're probably on the wrong side of the exponential.
5. "I would much rather have AI be the boss of me than vice versa." For example, Anish's agent is responsible for a 30 minute block in his calendar every week to pick someone he should reconnect with.
6. Improving individual productivity does not always improve a team's productivity. Coding has taught us the wrong lesson on this. For many functions, having individuals be 10x more productive doesn't mean the team will be 10x. e.g., If everyone goes in a different direction.
7. It's actually easier to start a hard company than an easy company... because they're all hard. At least the hard companies are interesting to work on and people want to work with you on them.
Excited to do a podcast recording with Josh next week to go deeper on some of this.
- Elon Musk
@elonmusk ·
Democrats will execute their plan to end democracy in America if they gain control
Alexandr Wang@alexandr_wang ·
if this isn’t personal superintelligence i don’t know what is
elvis@omarsar0 ·
Interesting paper from Anthropic and colleagues on agent security.
(bookmark it)
Poisoning 0.1% of the pretraining data planted a remote code execution backdoor in a CLI agent.
On a 4B model, the attack succeeded on roughly 92% of triggered prompts.
They pretrained Qwen3 models from scratch at 0.6B, 1.7B, and 4B, then post-trained each one into an agent with a clean pipeline.
The backdoor survived SFT, DPO, and RL for tool use. On clean inputs, the agent behaved normally.
The trigger can be an ordinary file path in the user's own request. Success rose with model size.
At a 0.0005% poison rate, about 2,500 documents in a 100B-token corpus, the trigger still fired on 1.4% of prompts in a single-seed run.
The poison states its rule in plain text, which makes it harder to filter out of training data.
https://arxiv.org/abs/2610.37741
Chat with Paper: https://academy.dair.ai/papers/agentic-backdoors-via-pretraining-poisoning-2610.37741
- Elon Musk
@elonmusk ·
Vote today!
Vox@Voxyz_ai ·
Found the fix!
UI/UX skills work on your repo's README too, and keep Codex and Claude Code from slowly 𝘁𝘂𝗿𝗻𝗶𝗻𝗴 𝗶𝘁 𝗶𝗻𝘁𝗼 𝗮 𝗰𝗵𝗮𝗻𝗴𝗲𝗹𝗼𝗴. The model branches out on its own toward whatever you need. That's why I love LLMs.
Send it this: Rewrite README.md for someone who has never seen this project. First sentence: what it is and who it's for. Then list what it can do in short bullets. End with the install steps, and put the requirements in a table. Move all version history into CHANGELOG.md.
Then polish it with a frontend design skill: npx impeccable install /impeccable clarify README.md /impeccable distill README.md
River AI@river_ai_inc ·
Custom losses are now live on the River API.
Write your objective in PyTorch. Our Symbolic Engine compiles and runs it end-to-end across GPUs, handling efficient execution and memory usage.
Learn more and see it in action with decision models and score centering: http://river.ai/custom-losses
Peter Steinberger 🦞@steipete ·
This is smart. Wild that this wasn’t the default. Unpredictable inference quality was usually why I recommended skipping the middleman.