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Our ECCV 2026 paper shows how to teach web-search agents how to do their job properly:
It’s not enough for an agent to always search. It has to learn when its own knowledge is insufficient, what to search for, and if the evidence it gets back is useful.
We call this Self-Verification via Reinforcement Learning (SVRL). 🧵 1/X
Douglas Gray retweeted
I’m a centrist in that I thought Obama’s Cheesecake Factory joke was funny and also I like Cheesecake Factory
This is the really interesting thing about local models and scaling test time compute.
Your laptop is idle most of the time, so you can just let them run indefinitely without racking up a huge anthropic bill.
This reminds me of the original Yahoo web directory, except now an LLM does the browsing.
Instead of nearest-neighbor retrieval, it navigates a hierarchy of topics, documents, and passages.
I really like that the index structure is human readable.
arxiv.org/abs/2610.35868
Anthropic should explore a lootbox pricing model.
$5000 to be the only one to see Mythos 6 try to solve some unsolved problem.
You might get a bucket of slop, or you might win a Nobel prize.
Dario Amodei: some tokens Claude produces are worth a few cents, others could be worth tens of millions of dollars.
His examples:
1. Telling someone to restart their Mac: the model has said it 10 million times, worth a dollar or less
2. Telling a pharma company to move an aromatic ring to the other end of a molecule: potentially tens of millions
3. Expect pay-for-results pricing, or labor-like pricing by the hour
4. It's a new industry, so many models will be tried
Today pricing is simpler: per the S-1, usage-based pricing was 80% of H1 2026 revenue and subscriptions 20%.
Full breakdown here:
theaiopportunities.com/p/ant…
This anecdote is like twitter dunk catnip.
You can be anti-AI and judge someone’s parenting all in one go.
I personally see nothing wrong with this. I’m sure I’ve done far worse.
AI version of “It’s difficult to get a man to understand something when his salary depends on his not understanding it”
I’m hopeful that with all this deep study of Claudish that the next generation of models will be much more comprehensible.
If this is true, it feels like it should be a bigger deal than VW diesel gate.
WTF, $3000 Samsung TV has an always-on mic, automatic content recognition, geo tracking, network mapping, and ships your data to Samsung + 400 partners.
Bro Rooted a brand-new 2025 Samsung TV after noticing absurdly targeted ads.
Firmware was locked down, so zero-days it was.
Got a root shell.
then found: always-on voice service, ACR, geo, full telemetry pipeline, and four separate DRM systems.
This isn’t a bug, It’s the product bro.
AI finding security bugs doesn’t surprise me. The attack surface is too vast for anyone to check everything.
But flaws in softmax and cross-entropy, after a decade of scrutiny by thousands of AI researchers? Shocking.
Need to spend more time with Gemini.
Every re-org or departure email I've read over the last 3 years sounds like it was written by ChatGPT.
At first I thought it was lazy. Then I realized it was intentional. Convey information in a positive light without telling you the real reasons or giving you any information to gossip or speculate about.
Sometimes you click the @grok icon and it loads from cache with no latency.
This looks cool. Have been trying to build this myself for a while. It’s harder than it sounds. Will give this a try later.
My agents sent me 355 hours of reading this week. I can't read that fast.
We're launching Spacesheep (YC F26) to fix that: agents' work lands in a space your team can actually understand and work on together.
Try it: tell any agent "share this to spacesheep.dev"
This is the first local coding model I’m interested in trying out.
Runs 131 tok/s on a M4 Pro Mac with 48GB unified memory!
Introducing Flint, @Recursive_SI’s first model: a fast local coding model built with our automated research system, the Eureka Machine.
• Best-in-class fast local coding
• Up to 2x faster than comparable models: 125+ tokens/sec on a MacBook Pro, 150+ on RTX Spark
• Built from scratch in under 4 months
Private, unmetered intelligence on your own machine. Open weights available now on @huggingface, run it locally on your favorite framework or try it for free on @opencode or @pidotdev.
My hometown.
Was bodyboarding here as a teenager when one of my friends spotted a shark fin. We sprinted to the shore, terrified, turned around and saw it was a dolphin fin.
Or was it?
Folks who think Apple is ignoring AI because they’re not participating in the great AI data center buildout are going to wake up one day very surprised.
Saw someone repost this and for a moment I thought he was saying that he coauthored 500 ICLR 2027 submissions.
Thankfully not the case, but it’s crazy that my brain thought that was plausible.
Looks like we have over 500 submissions to ICLR this year.
openreview.net/group?id=ICLR…