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curated · 2026-09-08

▸ 7 items · updated 3m ago
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2026-09-08 · Tue
21:06
14d ago
FEATUREDAI HOT (Curated Pool)· aihot-apiZH21:06 · 09·08
→OpenAI rolls out Astra to all paid-tier users
OpenAI pushed Astra to Plus, Pro, Business, and Enterprise users across Codex and ChatGPT Work. The post doesn't explain what Astra does or list any specs or pricing changes, but it links to a live demo.
#OpenAI
why featured
Featured · importance 72 · hook + resonance
editor take
OpenAI rolled Astra to all paid tiers, but the post doesn't say what it does or what changed.
sharp
The rollout is broad — Plus, Pro, Business, Enterprise, across both Codex and ChatGPT Work. But the post is one sentence and a demo link. No feature definition, no spec changes, no pricing. Astra could be a new interaction mode in Codex or a workflow capability in ChatGPT Work — right now you'd have to guess from the demo. I'd discount this until someone runs real tests.
HKR breakdown
hook ✓knowledge —resonance ✓
→ open source
72
SCORE
H1·K0·R1
18:02
14d ago
AI HOT (Curated Pool)· aihot-apiZH18:02 · 09·08
→Sam Altman responds to Navier-Stokes proof dispute: rival had only Euler result, coordination failed
Sam Altman posted about a dispute with an Anthropic researcher over publishing a Navier-Stokes proof. He says the rival only had the Euler result, coordination broke down, and they threatened plagiarism accusations. The post doesn't spell out the proof details or timeline.
#Sam Altman#Anthropic
editor take
Sam says the rival only had the Euler result, coordination broke down, and they got threatened with plagiarism—but no proof details or timeline.
HKR breakdown
hook —knowledge —resonance —
→ open source
39
SCORE
H0·K0·R0
16:00
14d ago
FEATUREDAI HOT (Curated Pool)· aihot-apiZH16:00 · 09·08
→Mistral's post-mortem on migrating 40k lines of Fortran 77 to C++ with AI agents
Mistral published an engineering post-mortem on using their own AI agents to migrate a 40k-line Fortran 77 codebase to C++. The piece focuses on three hard parts: getting agents to understand undocumented legacy logic, preserving numerical precision after translation, and designing a verification pipeline to catch bugs. The post doesn't disclose which model was used, total time spent, or how many manual fixes were needed. Treat this as a methodology reference, not a product launch.
#Code#Mistral
why featured
Featured · importance 72 · hook + knowledge
editor take
Mistral's post-mortem on migrating 40k lines of Fortran 77 to C++ with its own agents skips model name, time, and manual fixes—treat it as a methodology piece.
sharp
This one's worth opening because Fortran 77 to C++ migration is a real pain point in old-school industrial and financial shops—not a benchmark demo. Mistral gets the hard parts right: how to make agents understand undocumented legacy logic, how to preserve floating-point precision after translation, and how to build an automated verification pipeline to catch bugs. But the post skips a few numbers I'd want: which model they used, total project time, and how many manual fixes were needed. That makes this more of an engineering thought piece than a reproducible benchmark. If your team is doing similar migrations, the "get it running first, then align precision module by module" workflow is useful. If you're trying to evaluate Mistral's agent coding chops from this, there's not enough here yet.
HKR breakdown
hook ✓knowledge ✓resonance —
→ open source
72
SCORE
H1·K1·R0
07:20
14d ago
AI HOT (Curated Pool)· aihot-apiZH07:20 · 09·08
→Mathematician Buckmaster announces PDE blowup results aided by LLMs, details OpenAI communication
NYU mathematician Tristan Buckmaster and collaborator Levent Alpöge announced three finite-time blowup results for incompressible porous media, Boussinesq, and 3D incompressible Euler equations, all with smooth forcing. They relied heavily on LLMs (Claude, Codex, GPT-5.6 Sol, Astra) and verified proofs in Lean. Buckmaster called the Euler writeup "AI slop" and detailed his communication with OpenAI: an internal OpenAI model claimed a forced Navier-Stokes blowup proof, but Buckmaster believes the team used extensive human effort and compute, contrary to claims of "very little human input." The post does not disclose the details or verification status of OpenAI's proof.
#Code#Tristan Buckmaster#Levent Alpöge#OpenAI
editor take
Mathematician Buckmaster used LLMs to prove Euler blowup, calls his own paper 'AI slop,' and details OpenAI communication.
HKR breakdown
hook —knowledge —resonance —
→ open source
39
SCORE
H0·K0·R0
00:17
15d ago
AI HOT (Curated Pool)· aihot-apiZH00:17 · 09·08
→GPT-6 Astra Blender Control Tutorial: Three Methods and Pitfalls
The post does not disclose specific content; only the title indicates a tutorial on using GPT-6 Astra to control Blender, covering three methods and pitfalls.
#GPT-6 Astra#Blender
editor take
WeChat blocked the article body; only the title says GPT-6 Astra can control Blender for 3D modeling with three methods and pitfalls, but no details on how or how well.
HKR breakdown
hook —knowledge —resonance —
→ open source
20
SCORE
H0·K0·R0
00:00
15d ago
● P1AI HOT (Curated Pool)· aihot-apiZH00:00 · 09·08
→OpenRouter launches Linux sandbox and Files API for model command execution
OpenRouter added a server-side shell tool and Files API so any model can run commands inside a hosted Linux container. Sandbox time costs $0.0001 per second, billed with the request. Network is off by default; you can enable it with an allowlist. The Files API handles uploading inputs and downloading outputs. The shell tool supports both OpenAI and Anthropic tool specs—set engine: openrouter to force server-side execution. The post doesn't disclose container resource limits or max runtime per invocation.
#OpenRouter#OpenAI#Anthropic
why featured
Featured · importance 88 · hook + knowledge + resonance
editor take
OpenRouter now gives any model a hosted Linux sandbox to run commands and handle files, billed per second.
sharp
OpenRouter shipped two things: a shell tool that lets models execute commands inside a hosted Linux container, and a Files API for uploading and downloading files. Both sources are pulling from the same official blog post, so the coverage is consistent but there's no independent reporting to cross-check. A few things I'd flag. The compatibility layer is well thought out — they support both OpenAI's shell tool and Anthropic's bash tool, and setting engine: openrouter forces server-side execution regardless of which model you use. That saves you from having to set up your own sandbox. Pricing is $0.0001 per second of sandbox time, Files API included. That's cheap per request, but costs will stack up if a model goes into a long debugging loop or installs a bunch of packages. What's missing: details on isolation between containers, resource caps, and any rate limits. Also, no word on how reliably different models actually use this — some will likely produce working scripts on the first try, others will spam stderr and burn your sandbox budget.
HKR breakdown
hook ✓knowledge ✓resonance ✓
→ open source
88
SCORE
H1·K1·R1
00:00
15d ago
FEATUREDAI HOT (Curated Pool)· aihot-apiZH00:00 · 09·08
→OpenAI's 3x AI productivity gain might just be a machine that never sleeps
OpenAI researchers now supervise 3.14 agent-workdays per 8-hour human shift. Median daily inference spend jumped from $14 in March to $600 by August, with the 90th percentile burning $7,000/day. Tom Tunguz argues this 3x gain is a 24-hour machine shift, not smarter humans. Over half of 4–8 hour tasks still need human intervention, turning engineers into factory-floor troubleshooters. The post cites OpenAI's own research blog; no specific model names are disclosed.
#Code#OpenAI#Tom Tunguz#Theory Ventures
why featured
Featured · importance 82 · hook + knowledge + resonance
editor take
OpenAI's 3x productivity gain is just machines running 24/7, with median daily inference cost surging from $14 to $600.
sharp
Tom Tunguz breaks down OpenAI's own numbers in a way that cuts through the hype. The 3x productivity gain isn't smarter humans — it's one engineer supervising 3.14 agent-workdays of machine runtime per 8-hour shift. The median researcher's daily inference bill jumped from $14 in March to $600 by August, a 40x surge in under five months. The top 10% burn $7,000 a day, annualizing to $2.5 million per seat. The real problem is yield. Over half of 4-to-8-hour tasks still need human intervention. Engineers aren't doing creative architecture anymore — they're walking the plant floor clearing machine jams from overnight runs. Tunguz calls this a digital assembly line with a massive defect rate, and I think that's exactly right. What makes this useful is the CFO lens: the market hears "3x productivity" and expects miracles, but what you're actually buying is a second and third shift of machine time. The post cites OpenAI's own research blog, so the numbers are solid. The open question is how long this burn-rate-for-uptime model holds if defect rates don't improve.
HKR breakdown
hook ✓knowledge ✓resonance ✓
→ open source
82
SCORE
H1·K1·R1

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