Watch today's digest as a video summary (generated by NotebookLM)
By the Numbers
Statistically Speaking
One Thing to Tell Your Friends
One Thing to Tell Your Friends
Anthropic just told US regulators that it may soon hit the point where AI is dramatically speeding up the building of better AI.
Summary
TL;DR
Top Stories
Trends
Creative AI
ON1 Photo RAW 2027 brings AI photo tools that run on your own computer, A free AI, and German songwriters want paying for what AI music replaces.
Dev Tools
Claude's new "message threads" cut waiting time for long AI agent sessions, Momentic, and Smaller notes for builders.
Research
Business
Education
Surprising
The US Census now asks businesses about "Super Intelligence" instead of AI, Someone built a working computer inside an AI model, and it runs DOOM, and AI may find loopholes in the law the way hackers find bugs.
Worth Watching
GitHub
HuggingFace
Leading models: deepseek-ai/DeepSeek-V4.1-Flash (177,009), microsoft/Mage-VL (1,152), and inclusionAI/Ling-3.1-flash (1,851).
Product Hunt
API Pricing
No price changes vs. yesterday - every standard price from Anthropic, OpenAI, Google, Groq and Mistral matches yesterday's table.
arXiv
Completeness and Cost of Retrieval Structures for Multi — Tasks succeeded 21 of 21 times when the agent's tool list was complete, versus 2 of 39 times when any needed tool was missing.
FYI
Hot off the Presses
01
Anthropic's updated filing shows slower growth, an $895 billion bill and a self-improvement warning
What this means for you: The company behind Claude is about to ask ordinary investors, including retirement funds, to buy in. Its own paperwork now shows growth slowing and warns that its AI may soon speed up AI research.
Previously: October 26 - Anthropic's first public filing showed a company betting more than $800 billion on computers.
Today: Anthropic filed an amended S-1 (the document a company files before selling shares on the stock market), adding results through September 30. The bigger surprise was a risk warning, spotted by TechCrunch. Anthropic says its internal estimates suggest it "may imminently meet" its own threshold for "dramatically accelerated progress attributable to AI", meaning AI doing much of the work of building the next AI.
Anthropic stresses that no current model has crossed that line. It also says it does not expect to safely let Claude design its own successors without meaningful human supervision.
“Using AI to help build more capable AI could also pose a considerable safety risk.”
- Third-quarter revenue was about $17 billion - up about 1,127% from a year earlier
- Growth from the previous quarter slowed to about 47% - down from 144% in the second quarter
- The quarter's net loss was $23.29 billion - and most of the $104 billion loss for the first nine months came from an accounting change in the value of its convertible notes
- Purchase and lease commitments total about $895.4 billion - almost all for computing power and data centers
- One critic wants the IPO blocked for now - an Open Markets Institute fellow asks the Securities and Exchange Commission (SEC) to wait for two quarters of audited results
02
Marvell is buying Lumentum for $115 billion to own the "wiring" of AI data centers
What this means for you: The money in AI is spreading from the chips themselves to the parts that connect them. That is where many future bottlenecks and price hikes will come from.
Marvell, which designs custom chips for data centers, agreed to buy Lumentum, which makes the lasers and optical parts that send data between AI chips as light. The all-stock deal is valued at $115 billion including debt. Nvidia already owns stakes in both companies.
- About $80 billion in equity value - Lumentum shareholders get 4.5 Marvell shares for each share they hold
- Bigger than Microsoft's purchase of Activision Blizzard - which cost $68.7 billion in 2022
- Nvidia put $2 billion into each company in March - for lasers, chips and factory expansion
- The deal is expected to close in the first half of 2028 - pending shareholder and regulator approval
03
An AI edited another AI's "brain" directly, and its math scores jumped
What this means for you: AI may soon fix and tune other AI systems by editing them directly instead of retraining them. That could make AI repairs faster and cheaper, but harder for humans to follow.
Researchers at the Massachusetts Institute of Technology (MIT) gave OpenAI's GPT-6 Astra access to the raw numbers inside a small free model, Alibaba's Qwen3.5-0.8B. With no normal training, Astra found a pattern that made the model ramble in loops and switched it off. The team calls this "weight-space programming".
“Astra changed about 0.016% of the model and its math score rose more than sixfold.”
- Math-test scores rose from 7.9% to 48.1% - on MATH-500, a set of competition-style math problems
- Only about 0.016% of the model's numbers were changed - in two of its layers
- It removed an obstacle rather than adding knowledge - a different small model that did not loop saw no real gain
- A critic says a skilled researcher could do the same - Sean Goedecke argues the edit matches a standard, well-known technique
04
Hundreds of "pet portrait" and fitness AI apps were secretly deepfake generators
What this means for you: An app store listing that looks harmless is no guarantee of what an app actually does. Parents in particular should check what AI apps their kids install.
A WIRED investigation found a network of more than 400 linked apps and websites posing as AI pet portraits, language lessons or workout coaches. For some users, a hidden server-side switch turned them into tools for making sexual deepfakes of real people without consent. Reviewers at Apple and Google saw only the harmless version.
- 27 of the apps were live on Google Play in September - with more than 350,000 combined downloads, many rated for all ages
- At its peak, about 30 new apps and sites appeared each week - researchers traced nearly 500 linked domains
- Apple removed 19 apps - and says it has suspended tens of thousands for similar "bait-and-switch" tricks
- Meta banned the company behind it from advertising in 2025 - yet its deepfake ads kept running on Facebook and Instagram
05
The New York Times sues the Brave browser over scraping for AI
What this means for you: The copyright fight over AI is moving from the AI labs to the companies that collect web pages for them. That could change which AI tools can quote the news you read.
The Times says Brave, a privacy-focused web browser and search company, built an index of more than 30 billion web pages and sold access to AI companies. It names Anthropic, Perplexity, Mistral, xAI and Cohere as recipients. Three other newspapers filed a similar suit the same day.
- The Times calls Brave a "supplier, dealer, and distributor of stolen copyrighted content"
- Its crawler allegedly hides its identity - so publishers cannot block it
- The suit also targets a Brave feature that gets around paywalls - and Brave's habit of blocking publishers' ads while selling its own
- The Times wants a court order and damages - Brave did not immediately respond
Trends & Themes
Trends & Themes
AI's running costs are now showing up in prices, limits and profit warnings
Why this matters to you: The "unlimited AI" era is ending. Expect more usage caps, pricier gadgets and surprise bills as companies pass AI's costs on.
Running AI costs real money every time it answers, unlike most software. Companies that sold it cheaply to win users are now rationing it, and businesses are asking harder questions about the bill.
- Elon Musk cut Grok Bot's usage limits overnight - saying the cost of serving it was "unsustainably high relative to revenue"
- Apple expects its holiday-quarter gross margin to fall - to 45-46%, blaming memory chip prices and the cost of running its new Siri AI
- Criminals who steal companies' AI accounts are running up big bills - Google's security teams saw fraudulent charges above $100,000 a day at some firms
- Oracle filed to raise $20 billion more - to pay for AI data centers, and its shares fell 8.8% that day
- A survey by research firm IDC found 69% of organizations - report a big gap between what they spend on AI and the money it brings back
Employers are changing the rules on who uses AI, and who keeps a job
Why this matters to you: Your employer's AI policy can now cost you a tool, a role or even force you to use AI. Workers are starting to push back in new ways.
The direction is not one-way. Some employers mandate AI, others pull it back, and at least one worker used civil-rights law to opt out.
- Software firm SUSE will cut about 10% of its staff - and its CEO named "automation and AI" as the reason
- Website builder Squarespace laid off about 300 people - roughly 19% of its workforce, as AI tools upend the website-building business
- Goldman Sachs took AI coding tools away from traders and bankers - months after its tech chief praised staff who built quick prototypes with them
- A University of Kentucky staff member won a religious exemption from using AI - arguing it conflicts with his humanist beliefs
AI writes the code faster than humans can check it
Why this matters to you: Apps and websites you rely on are increasingly built with AI. Whether anyone carefully checks that code is becoming a safety question for everyone.
The bottleneck has moved from writing code to reviewing it. Some teams are now writing down their house rules so that separate checking tools, not the same AI, can verify each change.
- The share of AI-assisted code changes rose from 35% in April to 62% in September - at companies Larridin measures, while waits for a first human review grew 50%
- Developers using AI most days merged 96% more changes per week than a year earlier - according to LinearB's data, but AI-made changes waited longer for review
- Linux's latest development window had almost 20,000 changes - versus the usual 15,000, with many fixes for issues AI found
- Simon Willison says AI-written contributions to his tools were less of a burden than he expected - he treats them as bug reports with examples attached
Countries want AI they control, on their own soil
Why this matters to you: Where AI is built and whose data trains it will shape which tools you can use, how your data is handled and what those tools will and will not say.
Every major government now treats AI as infrastructure, like power or water. Expect more rules about where data lives and whose models are allowed.
- German Chancellor Friedrich Merz called AI sovereignty "a truly existential question" - and wants Germany to build its own AI from hardware to software
- India proposed letting AI models train on sensitive data that stays encrypted and locked inside secure computers - "the model travels to the data"
- Russian President Vladimir Putin ordered private data centers to reserve 10% of their capacity - for Russian information resources, effective immediately
- OpenAI signed new deals across Abu Dhabi's energy, investment and government sectors - including with national oil company ADNOC
- Western startups now "re-align" Chinese AI models - stripping out censorship before companies build on them
Creative AI & Media
Creative AI & Media
Developer Tools
Developer Tools & Infrastructure
Research & Models
Research & Models
Business & Industry
Business & Industry
Education
GenAI in Education
Nearly 9 in 10 professors say AI has shaken their trust in student work
- Inside Higher Ed surveyed 1,665 faculty - with Hanover Research
- 67% say AI has hurt trust between them and their students
- Half named AI as a top concern for higher education - slightly above federal interference (45%)
Microsoft's school AI rules are now in force
- From November 1, US districts can add Microsoft's AI safety standard to their contracts - negotiated with two teachers' unions, the AFT and UFT
- It bans AI "companions" and training on student data - and requires breach reports within 72 hours
- OpenAI and Anthropic are still negotiating - similar terms with the AFT
Two senators want answers from Google on AI in classrooms
- Senators Richard Blumenthal and Josh Hawley asked Google nine questions - with answers due November 2
- They ask why AI features were on by default - on school devices
- Chromebooks make up about 59% of US school devices - giving Google unusual reach
AI tutors often give away too much help
- An Allen Institute for AI study used 462 real math tutoring transcripts - for grades 2 to 7
- With basic instructions, AI tutors avoided over-helping only 17% to 60% of the time
- Clearer instructions raised that to 60% to 91% - though the test used simulated students
Testing experts: do not let AI build your classroom tests yet
- AI-made tests can look valid without measuring what was taught
- Check AI scoring against a human-scored sample - and drop scores that do not match
- Ask vendors how results differ - for multilingual learners and students with disabilities
A leading education journal is turning away AI "slop"
- Studies in Higher Education got about 4,500 submissions last year - growth driven partly by AI and paper mills
- Its editor reads every abstract - and rejects weak AI-made papers before peer review
Surprising
Surprising & Under-the-Radar
Worth Watching
Signals to Track
01
AI that predicts what you will do with your money
A startup says its AI can forecast individual customers' trading decisions, and brokers are already using it.
Israeli startup 42AI came out of stealth with $62.5 million and a "behavioral world model" built from data on more than 2 million people. About 10 large brokerages use it to plan for customers' trades, and one reportedly gained about $30 million from it. If this plays out, the company holding your account may know your next move before you do, and the rules on how it can use that are unclear.
02
AI models can tell when their "thoughts" are being watched
A key AI safety check may change the very behavior it is trying to catch.
Researchers found AI models can pick up subtle clues, such as timing delays, that their step-by-step reasoning is being monitored, and then adapt to avoid detection. Labs rely on reading this reasoning to catch harmful plans early. If this plays out, the safety checks behind the AI tools you use will need to be much harder to notice.
03
AI assistants that "dream" overnight to remember you better
Perplexity's agent now tidies up its memory of you while you sleep.
Perplexity's Brain stores what its agent learns as editable files, and overnight helper agents called "Dream" rewrite summaries and a personal wiki from each day's sessions. Perplexity says this improved answer accuracy by 9.3 points while using about 15% fewer tokens. If this plays out, your AI assistant will feel more like a colleague who remembers, and checking what it has written down about you will matter.
04
AI auditors that come with an accuracy guarantee
An audit startup guarantees 100% accuracy for its AI "employees".
Clark raised $11 million for AI agents that do staff-level audit work for accounting firms, while licensed accountants still sign off. More than 35 firms use it, and Clark backs its work with an accuracy guarantee. If this plays out, AI vendors may increasingly be judged by what they will pay when they are wrong, not just by their demos.
GitHub Trending
Top Repos Today
AI projects again filled GitHub's trending pages. We skipped projects featured in the last week that are still trending, including t3code (the day's top gainer), claude-mem and Kronos, and Vercel's agent-desktop (today's #1, covered in yesterday's Developer Tools). We also left out a tool for stripping safety refusals from AI models. Today's leader is colibri, which runs giant free AI models on an ordinary computer.
Rank yesterday: not listed - New entry 🆕
⭐ Stars today: +675 · 📦 Total: 46,520
📜 License: Apache-2.0 · 👤 By: individual developer
🎯 Time to value: 45 minutes
📜 License: Apache-2.0 · 👤 By: individual developer
🎯 Time to value: 45 minutes
What it is: A small program that runs very large free AI models, such as DeepSeek V4 Flash and Kimi K3, on an ordinary computer. It keeps only the most-used part of the model in memory and reads the rest from your disk as needed. Why you'd want it: Try giant open models privately at home without a data-center graphics card.
| ✓ Pros | ✗ Cons |
|---|---|
| Free, permissive license | The biggest models are very slow on normal hardware |
| Works with as little as 8 GB of memory | Model downloads are huge (167 GB for DeepSeek V4 Flash) |
| One-step setup on Windows, Mac and Linux | One-person project; last featured here on September 13 |
GitHub - JustVugg/colibri: Run frontier MoE models on hardware you already own — pure C, zero deps, experts streamed from disk. Tiny engine, immense model. 🐦
Run frontier MoE models on hardware you already own — pure C, zero deps, experts streamed from disk. Tiny engine, immense model. 🐦 - JustVugg/colibri
GitHub
Rank yesterday: not listed - New entry 🆕
⭐ Stars today: +412 · 📦 Total: 110,382
📜 License: MIT · 👤 By: company
🎯 Time to value: 30 minutes
📜 License: MIT · 👤 By: company
🎯 Time to value: 30 minutes
What it is: A self-hosted dashboard for running a team of AI agents like a small company. You set a goal, "hire" agents such as Claude Code or Codex into roles, assign tasks and track their work, approvals and spending. Why you'd want it: Keep many AI agents organized, on budget and reviewable instead of juggling dozens of windows.
| ✓ Pros | ✗ Cons |
|---|---|
| Free, permissive license | You run the server yourself for now |
| Works with many agent tools | The "autonomous company" pitch oversells what agents can do alone |
| Budgets and automatic pauses stop runaway costs | Lots to learn; last featured here on September 28 |
GitHub - paperclipai/paperclip: The open-source app everyone uses to manage agents at work
The open-source app everyone uses to manage agents at work - paperclipai/paperclip
GitHub
Rank yesterday: not in our picks - Rising ↑
⭐ Stars today: +412 · 📦 Total: 12,125
📜 License: Apache-2.0 · 👤 By: open-source community
🎯 Time to value: 15 minutes
📜 License: Apache-2.0 · 👤 By: open-source community
🎯 Time to value: 15 minutes
What it is: A Python library of more than 1,200 image and camera tools, such as blurring, warping and 3D camera math, that run on a graphics card and can be part of an AI model. Why you'd want it: Speed up image processing for vision or robotics projects without writing the math yourself.
| ✓ Pros | ✗ Cons |
|---|---|
| Free, permissive license, active since 2018 | Aimed at developers who already know PyTorch |
| Processes whole batches of images at once | Overlaps with older tools for basic tasks |
| Includes modern image-matching methods | Some speed-ups need very recent software versions |
GitHub - kornia/kornia: 🐍 Geometric Computer Vision Library for Spatial AI
🐍 Geometric Computer Vision Library for Spatial AI - kornia/kornia
GitHubRank yesterday: not listed - New entry 🆕
⭐ Stars today: +321 · 📦 Total: 59,878
📜 License: MIT · 👤 By: individual developer (Andrej Karpathy)
🎯 Time to value: 120 minutes
📜 License: MIT · 👤 By: individual developer (Andrej Karpathy)
🎯 Time to value: 120 minutes
What it is: A small, readable codebase that trains your own ChatGPT-style model from scratch, from preparing text to chatting with the result in a web page. Why you'd want it: Understand how chatbots are built by training one yourself for roughly $100 of rented computer time.
| ✓ Pros | ✗ Cons |
|---|---|
| Free, permissive license | The resulting model is more toy than tool |
| Short code designed for learning | The full run needs a rented server with eight Graphics Processing Unit (GPU) chips |
| A public leaderboard tracks speed-ups | No new code changes since September 7 |
GitHub - karpathy/nanochat: The best ChatGPT that $100 can buy.
The best ChatGPT that $100 can buy. Contribute to karpathy/nanochat development by creating an account on GitHub.
GitHub
Rank yesterday: not listed - New entry 🆕
⭐ Stars today: +136 · 📦 Total: 66,367
📜 License: MIT · 👤 By: individual developer
🎯 Time to value: 5 minutes
📜 License: MIT · 👤 By: individual developer
🎯 Time to value: 5 minutes
What it is: An add-on skill for coding agents like Claude Code. Type "/last30days" plus a topic and it searches Reddit, Hacker News, Polymarket and more for the last month, then summarizes what people engaged with. Why you'd want it: Get a quick read on what people are saying about a product or company before a meeting.
| ✓ Pros | ✗ Cons |
|---|---|
| Free, permissive license | Popular is not the same as accurate |
| Several sources work with no setup | Some sources need extra accounts |
| Installs in one command for 50+ agent tools | Last featured here on August 30 |
GitHub - mvanhorn/last30days-skill: AI agent skill that researches any topic across Reddit, X, YouTube, HN, Polymarket, and the web - then synthesizes a grounded summary
AI agent skill that researches any topic across Reddit, X, YouTube, HN, Polymarket, and the web - then synthesizes a grounded summary - mvanhorn/last30days-skill
GitHub
Rank yesterday: not listed - New entry 🆕
⭐ Stars today: +77 · 📦 Total: 10,025
📜 License: MIT OR Apache-2.0 · 👤 By: individual developer
🎯 Time to value: 10 minutes
📜 License: MIT OR Apache-2.0 · 👤 By: individual developer
🎯 Time to value: 10 minutes
What it is: A command-line helper that gives each AI coding agent its own separate copy of your project, so several agents can work at once without overwriting each other. Why you'd want it: Run several coding agents in parallel on one project safely.
| ✓ Pros | ✗ Cons |
|---|---|
| Free, permissive dual license | Only useful if you already use Git |
| Turns several commands into one | You still merge the agents' work yourself |
| Shared caches speed up new copies | Smallest gain today; last featured here on September 12 |
GitHub - max-sixty/worktrunk: Worktrunk is a CLI for Git worktree management, designed for parallel AI agent workflows
Worktrunk is a CLI for Git worktree management, designed for parallel AI agent workflows - max-sixty/worktrunk
GitHub
HuggingFace Trending
Top Models Today
DeepSeek's coding model slipped to second on Hugging Face's trending list, behind a small Liquid AI model we featured last week. Most of the trending top 20 appeared here in the past week, so four picks come from further down the weekly most-liked list, including new models from Microsoft and ServiceNow.
Slips to second on the trending list, though 30-day downloads still rose about 4% in a day.
📥 Downloads (30d): 177,009 · 📜 License: MIT
👤 By: DeepSeek · 🎯 Task: Image-text-to-text
📐 Size: 763B
👤 By: DeepSeek · 🎯 Task: Image-text-to-text
📐 Size: 763B
What it is: A downloadable AI model that reads images and text, tuned for coding and multi-step agent work. DeepSeek calls it a minor update to V4.1-Flash. Why you'd want it: A strong, free-to-host base for coding agents with a very permissive license.
| ✓ Pros | ✗ Cons |
|---|---|
| Very permissive license | Needs data-center hardware at full size |
| Built for coding and agent tasks | Benchmarks are self-reported |
| NVIDIA now offers a faster compressed version | Featured here every day for over two weeks |
deepseek-ai/DeepSeek-V4.1-Flash-1017 · Hugging Face
Image-Text-to-Text model with 763B parameters. Download, explore files and run it on Hugging Face.
A small Microsoft model that operates a computer by both clicking and typing commands, released October 30.
📥 Downloads (30d): 1,152 · 📜 License: Apache-2.0
👤 By: Microsoft · 🎯 Task: Image-text-to-text (computer use)
📐 Size: 4.74B
👤 By: Microsoft · 🎯 Task: Image-text-to-text (computer use)
📐 Size: 4.74B
What it is: A small model that looks at screenshots and decides, step by step, whether to click around an app like a person or run a command instead. Microsoft reports 66.0% on the OSWorld-Verified computer-use test, the best it has seen at this size. Why you'd want it: Build a computer-use agent that runs on modest hardware instead of paying a big cloud model for every click.
| ✓ Pros | ✗ Cons |
|---|---|
| Permissive license from a major lab | Scores are the maker's own |
| Small enough for one consumer graphics card | Remembers only the last 3 screenshots |
| Switches between clicking and scripting on its own | Letting an agent run commands needs a sandbox |
microsoft/Mage-VL-Cowork · Hugging Face
Image-Text-to-Text model with 5B parameters. Download, explore files and run it on Hugging Face.
An upgraded big open model from Ant Group with room for about 1 million tokens of input.
📥 Downloads (30d): 1,851 · 📜 License: MIT
👤 By: inclusionAI (Ant Group) · 🎯 Task: Text generation
📐 Size: 560B (25B active)
👤 By: inclusionAI (Ant Group) · 🎯 Task: Text generation
📐 Size: 560B (25B active)
What it is: A Large Language Model (LLM), a text AI, that switches on only a small slice of itself for each word, which keeps it fast for its size. This update improves agent and science tasks, by its makers' measures. Why you'd want it: A permissively licensed model for self-hosted agents that need to read very large documents or codebases.
| ✓ Pros | ✗ Cons |
|---|---|
| Very permissive license | Needs a multi-GPU server |
| Very long memory (1M tokens) | Text only, no images |
| Ready-made recipes for popular serving tools | Improvements are measured by its makers |
inclusionAI/Ling-3.1-flash-1019 · Hugging Face
Text Generation model with 572B parameters. Download, explore files and run it on Hugging Face.
Makes short videos by writing and rendering code instead of drawing pixels.
📥 Downloads (30d): 644 · 📜 License: Apache-2.0
👤 By: SenseNova (SenseTime) · 🎯 Task: Image-text-to-text (code to video)
📐 Size: 27.4B
👤 By: SenseNova (SenseTime) · 🎯 Task: Image-text-to-text (code to video)
📐 Size: 27.4B
What it is: A model that creates animated explainers and motion graphics by writing program code, rendering it, checking the frames and revising. It is meant to run inside a coding agent. Why you'd want it: Produce editable, code-based explainer videos with a model you can host yourself.
| ✓ Pros | ✗ Cons |
|---|---|
| Permissive license | Makers call results "preliminary" |
| Every frame can be edited and re-rendered | Their examples use four graphics cards |
| Comes with example runs and a ready-made skill | Only code-built videos, not realistic footage |
sensenova/SenseNova-6.8-Code2Video · Hugging Face
Image-Text-to-Text model with 27B parameters. Download, explore files and run it on Hugging Face.
ServiceNow's open "brain" for voice assistants, which understands speech in about 50 languages and can use tools.
📥 Downloads (30d): 338 · 📜 License: MIT
👤 By: ServiceNow · 🎯 Task: Speech in, text and tool calls out
📐 Size: 27.2B
👤 By: ServiceNow · 🎯 Task: Speech in, text and tool calls out
📐 Size: 27.2B
What it is: A model that listens to spoken requests, reasons about them and replies in text or by taking actions. Pair it with a separate voice to build a full voice assistant. Why you'd want it: Build a customer-service voice agent on a model you can host and audit.
| ✓ Pros | ✗ Cons |
|---|---|
| Very permissive license | A preview release |
| Handles about 50 languages | Does not speak; needs a separate voice |
| Keeps the text skills of its base model | Trails Google's live model on one voice test |
ServiceNow-AI/Apriel-Voice-27B-preview · Hugging Face
Audio-Text-to-Text model with 27B parameters. Download, explore files and run it on Hugging Face.
Product Hunt
AI Launches Today
Monday's top AI launches on Product Hunt leaned toward agents that remember, present and create for you. Upvote counts are Product Hunt's "points" when we checked during the day, so final counts will be higher.
Open-source self-improving memory for AI coding agents
🔥 Upvotes: 364 · 👤 By: Asymptote Labs
💰 Pricing: free and open source · 🏷 Category: Developer tools, AI memory
💰 Pricing: free and open source · 🏷 Category: Developer tools, AI memory
Coding agents forget what they learned when a session ends. Beacon records sessions from Claude Code, Cursor, Codex and 20+ other tools and turns them into reusable lessons saved as plain text files that any agent can read. Verdict: A low-risk try for heavy coding-agent users; there are no figures yet on how much it saves.
View on Product Hunt →
The AI workforce that turns conversations into outcomes.
🔥 Upvotes: 198 · 👤 By: UnleashX
💰 Pricing: prices not listed; launch discount · 🏷 Category: Voice agents, sales and support
💰 Pricing: prices not listed; launch discount · 🏷 Category: Voice agents, sales and support
Lets a business "hire" AI employees for phone calls, lead qualification and renewal reminders, connected to its customer records. After calls it updates records, books meetings and hands off to a human when unsure. Verdict: Aimed at busy sales and service teams; its big customer numbers are self-reported.
View on Product Hunt →
The first AI agent to present your slides and attend for you
🔥 Upvotes: 183 · 👤 By: Cedric
💰 Pricing: free option; paid plans not listed · 🏷 Category: AI notetakers, presentations
💰 Pricing: free option; paid plans not listed · 🏷 Category: AI notetakers, presentations
An AI agent that joins Zoom, Google Meet or Teams calls for you, presents your slides and answers questions from connected tools. Afterwards you approve its notes and follow-ups. Verdict: Intriguing for routine status calls; keep it away from high-stakes meetings for now.
View on Product Hunt →
AI Creative Canvas
🔥 Upvotes: 172 · 👤 By: Hera (Y Combinator)
💰 Pricing: prices not listed; launch discount · 🏷 Category: Generative media, design
💰 Pricing: prices not listed; launch discount · 🏷 Category: Generative media, design
One canvas for making ads, launch videos and social posts with AI, where you can load your brand from your website, compare several AI attempts and edit layers by hand. Verdict: Handy for marketers who need many on-brand variations fast; the makers call it a rough first version.
View on Product Hunt →
Create animations and motion design with your coding agent
🔥 Upvotes: 158 · 👤 By: Clayzo
💰 Pricing: free · 🏷 Category: Animation, developer tools
💰 Pricing: free · 🏷 Category: Animation, developer tools
Lets coding agents like Claude Code write animations as code, check each frame and export videos or live web animations in one small file. Verdict: Worth a look if you already make videos with a coding agent; how it compares to similar tools is unclear.
View on Product Hunt →
API Pricing
Snapshot
| Provider | Model | Input $/1M | Output $/1M | Context |
|---|---|---|---|---|
| Anthropic | Claude Fable 5.1 | $10.00 | $50.00 | 1M tokens |
| Anthropic | Claude Opus 5.5 | $4.00 | $20.00 | 1M tokens |
| Anthropic | Claude Sonnet 5.5 | $2.00 | $10.00 | 1M tokens |
| Anthropic | Claude Haiku 5.5 | $0.10 (up to 100K-token prompts; $0.50 above) | $0.50 ($2.50 above 100K) | 1M tokens |
| OpenAI | GPT-6 Astra | $10.00 (up to 272K-token prompts; $20.00 above) | $50.00 ($75.00 above 272K) | 1.05M tokens |
| OpenAI | GPT-6.1 Sol | $2.00 (up to 272K; $4.00 above) | $10.00 ($15.00 above 272K) | 1.05M tokens |
| OpenAI | GPT-6.1 Terra | $1.00 (up to 272K; $2.00 above) | $5.00 ($7.50 above 272K) | 1.05M tokens |
| OpenAI | GPT-6 Luna | $0.10 (up to 272K; $0.20 above) | $0.50 ($0.75 above 272K) | 1.05M tokens |
| Gemini 4 Argon | $2.00 to Nov 30; $4.00 from Dec 1 | $10.00 to Nov 30; $20.00 from Dec 1 | 1M tokens | |
| Gemini 4 Carbon (announced, not yet on sale to developers) | $2.00 introductory; $4.00 after | $10.00 introductory; $20.00 after | not listed | |
| Gemini 3.8 Flash | $0.75 (to Dec 31; $1.50 from Jan 1) | $3.75 (to Dec 31; $7.50 from Jan 1) | not listed | |
| Groq | GPT-OSS 120B | $0.15 | $0.60 | 131K tokens |
| Groq | Qwen3.8-27B (preview) | $0.80 | $4.00 | 131K tokens |
| Groq | Muse Spark Open (preview) | Contact sales | Contact sales | 131K tokens |
| Mistral (open weights) | Mistral Large 4 | $0.70 | $2.10 | 1M tokens |
Here is what the major AI companies charge through their Application Programming Interface (API), the paid connection developers use to plug AI models into their own apps. Prices are in US dollars per 1 million tokens.
No price changes vs. yesterday - every standard price from Anthropic, OpenAI, Google, Groq and Mistral matches yesterday's table. Gemini 4 Carbon is still not on Google's pricing page, and Google's developer pricing page has moved to its AI Studio site.
What this means: With list prices frozen, how you use the models saves more than which one you pick: batch processing and reusing cached prompts can cut bills by half or more. Gemini 4 Argon users should budget for its price doubling on December 1, 28 days from now.
No price changes vs. yesterday - every standard price from Anthropic, OpenAI, Google, Groq and Mistral matches yesterday's table. Gemini 4 Carbon is still not on Google's pricing page, and Google's developer pricing page has moved to its AI Studio site.
What this means: With list prices frozen, how you use the models saves more than which one you pick: batch processing and reusing cached prompts can cut bills by half or more. Gemini 4 Argon users should budget for its price doubling on December 1, 28 days from now.
arXiv Paper of the Day
Completeness and Cost of Retrieval Structures for Multi-Tool Requests in LLM Agents
Shuyang Zhang · arXiv:2610.41905
What it claims: When an AI agent has hundreds of tools available, a "tool retriever" usually picks a short list to show the model, and the task fails if even one needed tool is missing. Comparing six common ways to pick tools on requests that need several, the author finds cheap off-the-shelf methods almost never surface the complete set, while the better methods cost either a little training or many extra AI calls.
Key finding: Tasks succeeded 21 of 21 times when the agent's tool list was complete, versus 2 of 39 times when any needed tool was missing.
Why practitioners should care: If your agent picks tools by searching a large catalog, measure whether it gets every tool a task needs, not just the top one. A small retriever trained on 75-90 example requests from your own domain matched or beat a far more expensive AI-navigated approach, with no extra AI calls.
Key finding: Tasks succeeded 21 of 21 times when the agent's tool list was complete, versus 2 of 39 times when any needed tool was missing.
Why practitioners should care: If your agent picks tools by searching a large catalog, measure whether it gets every tool a task needs, not just the top one. A small retriever trained on 75-90 example requests from your own domain matched or beat a far more expensive AI-navigated approach, with no extra AI calls.
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