Watch today's digest as a video summary (generated by NotebookLM)
By the Numbers
Statistically Speaking
$6
billion went to license the technology, plus
NVIDIA Paid ~$12 Billion for Poolside - Without Actually Buy
Top Story
109
employees received offers to join NVIDIA, while
NVIDIA Paid ~$12 Billion for Poolside - Without Actually Buy
One Thing to Tell Your Friends
One Thing to Tell Your Friends
AI companies are buying millions of second-hand physical books, slicing off the spines, running the pages through a scanner, and throwing the books away - just to get text written before AI existed.
Summary
TL;DR
Top Stories
Trends
The Assistant Is Moving Onto Your Own Computer, "Context, Not Compute" Is the New Bottleneck, and The Backlash Against Low.
Creative AI
Turn a Topic Into a Finished Short Video, Automatically and DeepMind's Generalist Game Agent Keeps Widening.
Dev Tools
The Real Cost of a Coding Model Is Cost-Per-Result, Not Per, llm, and NoBuzz.
Research
Business
Surprising
The Productivity Tool That Slows You Down, Outsourcing Your Thinking Can Make You Learn More, and The Debate: Is AI Text Watermarking Free and Good, or Doomed?.
Worth Watching
Apache Maka - Local-First Agents Get an Open, Cursor Opens Its Plugin Standard, and AI Agents Get Graded on Real Research Work.
GitHub
Leading repos: mattpocock/skills (+3,368), obra/superpowers (+789), and harry0703/MoneyPrinterTurbo (+1,187).
HuggingFace
Leading models: Qwen/Qwen3.8 (1,726,651), MiniMaxAI/MiniMax (3,614,443), and meta-models/Muse-Glimmer (505,113).
Product Hunt
API Pricing
Prices are holding steady from yesterday, with no changes flagged across the major providers.
arXiv
Learning When to Think: Adaptive Reasoning for Test — On grade-school math it cut token use by 76%, and on harder math it held accuracy (0.782 vs 0.796) while cutting average response length 41%.
FYI
Hot off the Presses
01
NVIDIA Paid ~$12 Billion for Poolside - Without Actually Buying It
What this means for you: The biggest AI companies are running out of easy ways to buy their way to the front, so they are inventing stranger deals - and that reshapes who controls the tools you will use.
NVIDIA, the company that makes most of the world's AI chips, struck a deal with Poolside (a startup building AI that writes software) that is neither a purchase nor a normal hire. The founders coined a name for it: a "reverse-execuhire." Instead of buying the company, NVIDIA licensed Poolside's core "Model Factory" technology and offered jobs to 109 of its staff, while the founders stayed independent to chase a separate research idea.
The trigger was raw scale. Poolside missed a January funding window to lock in a 40,000-chip cluster, and training a frontier model now needs far bigger clusters than that, limited by both money and available data-center space.
“$6 billion for the technology, $1 billion for a stake, and 109 people - but the founders kept the company.”
- $6 billion went to license the technology, plus a $1 billion investment at a $12 billion valuation
- 109 employees received offers to join NVIDIA, while the two founders stayed out
- A separate company (PIC) spun off to build a 7-gigawatt "neocloud" - roughly the power draw of a mid-size city
$6
billion went to license the
109
employees received offers to join
02
AI Companies Are Destroying Physical Books to Train Models
What this means for you: The race for "clean" training data now has a physical cost - and once a rare book is scanned and binned, that copy is gone for good.
Anna's Archive, a group that preserves digital copies of books, reports that AI companies are quietly buying large batches of second-hand books to get text written before 2022 - text "untouched by machines." The common bulk-scanning method cuts the spine off a book and feeds the loose pages through a high-speed scanner, which destroys it.
The group is calling for a global scan-it-yourself effort to save copies before they vanish, and is offering to help cover scanning costs.
- Pre-2022 text is the prize - it is human-written, before AI-generated content flooded the web
- Bulk scanning is destructive by design - de-spining is faster than careful page-turning
- The counter-move is volunteers - the group argues 10 million people scanning one book each preserves 10 million works
03
ChatGPT Quietly Rewired How It Searches the Web
What this means for you: If you run a website or a business, whether ChatGPT ever mentions you now depends on hidden rules that can change overnight with no warning.
Analytics firm Promptwatch noticed that ChatGPT's use of the "site:" trick (which limits a search to one website) jumped from under 0.5% of queries to 16-17% on August 8th, matching the rollout of OpenAI's GPT-5.6 model. The likely cause is not users typing anything different. OpenAI appears to have given ChatGPT an internal search tool that can quietly restrict results to chosen websites.
The same update seems to have cut how often ChatGPT cites Reddit, suggesting the model is being steered toward preferred sources.
“Use of the site: operator jumped from under 0.5% of ChatGPT queries to 16% overnight.”
- From under 0.5% to 16% of queries now scope to specific sites
- This is "GEO" - search-engine optimization, but for AI chatbots instead of Google
- The rules are invisible - businesses cannot see or verify how the model picks sources
04
DeepMind Takes Its Game-Playing AI Into EVE Online
What this means for you: Today's AI is great at quick answers but bad at long plans; this project is a bet on fixing that, which is what agents need before they can run multi-day tasks for you.
Google DeepMind marked 15 years of teaching AI through games and announced a new partnership inside EVE Online, a sprawling space game where events unfold over weeks. Past milestones went from mastering Atari games in 2015 to beating world champions at Go, and last year's SIMA 2, a single agent that reads a game screen, understands plain-language instructions, and plays several games it was not built for.
EVE is harder than a board game in a way that matters for real work: it demands memory beyond a single session and planning across weeks or months.
- From Atari (2015) to EVE Online (2026) - the target shifted from high scores to human-like understanding
- The hard part is now time - continual learning and long-horizon planning, not raw reflexes
- Why it matters - the same skills let a work assistant carry a project across many days
Trends & Themes
Trends & Themes
The Assistant Is Moving Onto Your Own Computer
Why this matters to you: A wave of new tools keeps your work data on your machine instead of a company's servers, which means more privacy - and assistants that actually know your context.
The common bet is that models are already smart enough, and the real edge is context - knowing your projects, people, and open tasks - kept private on your device. Expect "runs locally" to become a headline feature, not a niche one.
- ShogunAI and Project SKY both launched as desktop assistants that read your screen and files locally, not in the cloud
- NobodyWho and HyNote pitch on-device AI where "notes never leave your machine"
- Apache Maka (an Apache Software Foundation incubating project) is a local-first workspace for running agents on your own hardware
"Context, Not Compute" Is the New Bottleneck
Why this matters to you: The people getting the most from AI are not paying for bigger models - they are organizing what the model knows about them.
The lesson repeating across sources: how you feed and frame a model now decides results more than which model you pick.
- Ben Tossell built a personal setup from plain text files (identity, preferences, memory) and found small files beat elaborate memory systems
- A GLM-5.3 cost guide pushes a "six-line handoff" - a short clean brief that lets a fresh model start from the task, not a long transcript
- ShogunAI's whole pitch is that scattered context, not intelligence, is what holds assistants back
The Backlash Against Low-Effort AI Content Is Hardening
Why this matters to you: As AI-written filler spreads, people are building reflexes and tools to tune it out - which changes how your own writing gets read.
The pattern: trust is shifting from "sounds polished" to "was clearly worth a human's time."
- An essay on "AI-blindness" describes a brain reflex that automatically skips AI-generated documents, like ignoring web banner ads
- A new Claude skill called NoBuzz exists purely to strip AI's theatrical phrasing back into plain English
- The watermarking debate reheated (covered August 13), with a fresh argument that labeling AI text is essentially free
Benchmarks Are Getting Caught Gaming Themselves
Why this matters to you: The scores companies use to sell you AI can be inflated by memorization, so a "record-breaking" model may not be better at your task.
The takeaway: treat leaderboard wins with suspicion unless the test is fresh and audited.
- A speech-recognition study found top-scoring models often reproduce known-wrong reference transcripts 18-30% of the time, a sign of memorizing the test
- On one benchmark, models recovered 30-40% of numbers that had been silenced in the audio - they guessed from the test, not the sound
- New research benchmarks like DeltaML-Bench are being built to test agents on real, unseen work rather than familiar exam sets
Creative AI & Media
Creative AI & Media
Turn a Topic Into a Finished Short Video, Automatically
Try it: GitHub: harry0703/MoneyPrinterTurbo
- MoneyPrinterTurbo takes a topic or keyword and produces an HD short video end to end - script, footage, voiceover, and captions
- It is climbing GitHub fast (+1,187 stars today) as creators chase faceless short-form content
- The catch - output is templated and generic, useful as a first draft rather than a finished piece
DeepMind's Generalist Game Agent Keeps Widening
- SIMA 2 (DeepMind's Gemini-powered game agent) already plays across No Man's Sky, Valheim, and Hydroneer from the screen alone
- The EVE Online work (see Top Stories) pushes it toward persistent, long-running worlds
- Why creators should watch - the same "understand a live scene and act" skill underpins future interactive media and NPCs
Developer Tools
Developer Tools & Infrastructure
Research & Models
Research & Models
Get a Trustworthy "Am I Right?" Score From a Closed Model
- The problem - hosted models rarely tell you honestly how confident they are, and you cannot see inside them
- The fix - train a small helper classifier on a modest labeled set, combining existing confidence scores with the correctness of similar past questions
- Why it matters - even a little labeled data turns vague uncertainty into a usable "trust this answer or not" signal
A Cheaper Recipe for Reading a Model's "Insides"
- Mechanistic tomography unifies scattered interpretability tricks into one framework for measuring what a model is doing internally
- Key finding - cheap, sparse measurements can recover a model's internal effect maps more efficiently than exhaustively probing every part
- Why practitioners care - it makes inspecting and steering model behavior more affordable, part of a shift toward reading models rather than just asking them
When Should You Retrain a Live Model? Big Study Has an Answer
- 3,933 controlled experiments compared retraining on a schedule, on an error threshold, and on drift detection
- Headline result - whether the model updates incrementally matters more than which retraining trigger you choose
- Why it matters - it gives overloaded ML teams evidence-based guidance instead of guesswork on a real cost line
A Smarter Momentum Rule for Training
- DeltaMomentum replaces the one-size-fits-all "forgetting rate" in optimizers like Adam with a per-direction rate
- How - it uses the key-value structure inside linear layers and the classic delta rule to clear stale directions faster
- Why it matters - a cheap tweak (no matrix inversion) that can make training more stable when data is lopsided
Business & Industry
Business & Industry
Surprising
Surprising & Under-the-Radar
The Productivity Tool That Slows You Down
- An engineer describes "AI-blindness" - his brain now auto-skips documents that read as AI-written, the way we ignore banner ads
- Why it is surprising - tools meant to boost output flood the workplace with shallow content people learn to tune out
- He nearly missed a genuinely useful AI-written note because the filter had become automatic
Outsourcing Your Thinking Can Make You Learn More
- A developer used ChatGPT to finally understand quaternions (a tricky bit of 3D math) after years of failing with textbooks
- His counterintuitive takeaway - "learning doesn't stop just because I outsource a bunch of thinking to AI. It pushes me to learn more"
- Why it is surprising - it cuts against the common worry that leaning on AI always dulls understanding
The Debate: Is AI Text Watermarking Free and Good, or Doomed?
- One side (a fresh argument today) says watermarking costs almost nothing and a 20-million-user test showed no drop in quality
- The other side (August 13) argues determined users can always strip a watermark, so it will never be reliable
- Where they agree - the mark can flag that text touched AI, but cannot pin it to a specific person
The Debate: Should You Still Build a Terminal App?
- One view - AI coding agents make graphical apps so cheap to build that shipping a plain command-line tool is now a missed opportunity
- The other view - terminal tools are still faster to make and fine for personal scripts
- Why it is under-the-radar - it hints at a coming flood of small, custom desktop apps built by non-specialists
Worth Watching
Signals to Track
01
Apache Maka - Local-First Agents Get an Open-Source Home
A trusted open-source foundation backing on-device agents is a sign the private-assistant trend is going mainstream.
Apache Maka is an incubating project for running and managing AI agents on your own machine with data staying local. Because it sits under the Apache Software Foundation, it could become the neutral standard for local agents. If it works, ordinary users get powerful assistants without handing their files to a cloud.
02
Cursor Opens Its Plugin Standard
Whoever sets the plugin format for AI code editors controls a fast-growing developer ecosystem.
Cursor published an official plugin specification plus first-party plugins so developers can extend its AI editor. A shared plugin standard invites a marketplace of add-ons. For everyday developers, it means more ready-made tools bolted onto the editor they already use.
03
AI Agents Get Graded on Real Research Work
Benchmarks built from genuine unsolved tasks, not textbook problems, will finally show what agents can actually do.
New evaluations like DeltaML-Bench test whether AI agents can handle real machine-learning research chores rather than tidy exam questions. Honest, hard benchmarks are how hype gets separated from ability. If agents pass these, the promise of AI "coworkers" gets a lot more credible.
04
Defending Against Leaked Prompts
As assistants read more of your private context, quietly preventing that context from leaking becomes essential plumbing.
New defensive research looks at "inadvertent context leakage," where a model accidentally reveals private information from earlier in a conversation. As local assistants ingest your emails and files, this kind of safeguard moves from nice-to-have to mandatory. It is the unglamorous work that decides whether people trust these tools with real data.
GitHub Trending
Top Repos Today
Rank yesterday: not in top 6 - New entry 🆕
⭐ Stars today: +3,368 · 📦 Total: 229,389
📜 License: MIT · 👤 By: individual
🎯 Time to value: 10 minutes
📜 License: MIT · 👤 By: individual
🎯 Time to value: 10 minutes
What it is: A curated, open collection of reusable "skills" pulled straight from a well-known educator's own AI-agent setup. Each skill is a packaged instruction set that tells a coding agent how to do a specific job well. You drop the ones you want into your agent and it gains those abilities. Why you'd want it: It is a fast way to make your AI coding assistant sharper without writing all the guidance yourself.
| ✓ Pros | ✗ Cons |
|---|---|
| Ready-made, battle-tested skills | Tuned to one person's workflow |
| MIT-licensed and easy to fork | Quality varies across skills |
| Works with common coding agents | You still have to curate what fits |
GitHub - mattpocock/skills: Skills for Real Engineers. Straight from my .agents directory.
Skills for Real Engineers. Straight from my .agents directory. - mattpocock/skills
GitHub
Rank yesterday: #6 - Rising ↑
⭐ Stars today: +789 · 📦 Total: 275,638
📜 License: MIT · 👤 By: individual
🎯 Time to value: 20 minutes
📜 License: MIT · 👤 By: individual
🎯 Time to value: 20 minutes
What it is: A framework that gives AI coding agents structured abilities plus a repeatable method for driving them. It bundles both the capabilities and the process for using them. The goal is to make agent-led development consistent instead of hit-or-miss. Why you'd want it: It offers a full playbook, not just tools, for people who want agents to handle real development work.
| ✓ Pros | ✗ Cons |
|---|---|
| Method plus tools, not just tools | Opinionated workflow to learn |
| Very popular and active | Overkill for tiny tasks |
| Free and open source | Assumes comfort with agents |
GitHub - obra/superpowers: An agentic skills framework & software development methodology that works.
An agentic skills framework & software development methodology that works. - obra/superpowers
GitHub
Rank yesterday: not in top 6 - New entry 🆕
⭐ Stars today: +1,187 · 📦 Total: 113,864
📜 License: MIT · 👤 By: individual
🎯 Time to value: 15 minutes
📜 License: MIT · 👤 By: individual
🎯 Time to value: 15 minutes
What it is: A Python tool that turns a topic or keyword into a finished HD short video. It writes the script, sources footage, adds a voiceover, and stitches it together automatically. It targets creators making faceless short-form clips. Why you'd want it: It removes almost all the manual work of producing simple short videos at volume.
| ✓ Pros | ✗ Cons |
|---|---|
| Full pipeline in one tool | Output looks templated |
| Fast, high volume | Generic footage and voice |
| Free and self-hosted | Needs editing to stand out |
GitHub - harry0703/MoneyPrinterTurbo: 利用 AI 大模型和自动化工作流，根据主题或关键词一键生成高清短视频。Generate HD short videos from a topic or keyword with an automated AI workflow.
利用 AI 大模型和自动化工作流，根据主题或关键词一键生成高清短视频。Generate HD short videos from a topic or keyword with an automated AI workflow. - harry0703/MoneyPrinterTurbo
GitHub
Rank yesterday: #5 - Rising ↑
⭐ Stars today: +905 · 📦 Total: 28,671
📜 License: Custom (source-available) · 👤 By: startup
🎯 Time to value: 30 minutes
📜 License: Custom (source-available) · 👤 By: startup
🎯 Time to value: 30 minutes
What it is: The home of the MAX inference engine and the Mojo programming language, aimed at running AI fast across different chips from one codebase. Mojo went fully open source recently, which keeps drawing attention. It targets teams that serve AI models at scale. Why you'd want it: It promises high performance without locking you to one chip vendor.
| ✓ Pros | ✗ Cons |
|---|---|
| One codebase across processors and Graphics Processing Units (GPUs) | Steep learning curve |
| Fast, serving-focused | Newer, smaller ecosystem |
| Mojo now open source | Some parts under custom terms |
GitHub - modular/modular: The Modular Platform (includes MAX & Mojo)
The Modular Platform (includes MAX & Mojo). Contribute to modular/modular development by creating an account on GitHub.
GitHub
Rank yesterday: not in top 6 - New entry 🆕
⭐ Stars today: +140 · 📦 Total: 68,628
📜 License: MIT · 👤 By: individual
🎯 Time to value: 25 minutes
📜 License: MIT · 👤 By: individual
🎯 Time to value: 25 minutes
What it is: A meta-harness for building and deploying autonomous AI workflows. It orchestrates several agents and tools into coordinated pipelines. It is for people wiring multiple agents together into one system. Why you'd want it: It handles the plumbing of getting many agents to work as a team.
| ✓ Pros | ✗ Cons |
|---|---|
| Coordinates multi-agent pipelines | Complex to configure |
| Popular and flexible | Assumes agent experience |
| Free and open source | Sparse beginner docs |
GitHub - ruvnet/ruflo: 🌊 The original agent meta-harness. Deploy intelligent multi-player swarms, coordinate autonomous workflows, and build conversational AI systems. Features adaptive memory, self-learning intelligence, RAG integration, and native Claude Code / Codex / Hermes and many more Integrated
🌊 The original agent meta-harness. Deploy intelligent multi-player swarms, coordinate autonomous workflows, and build conversational AI systems. Features adaptive memory, self-learning intelligence…
GitHub
Rank yesterday: not in top 6 - New entry 🆕
⭐ Stars today: +141 · 📦 Total: 2,001
📜 License: Apache-2.0 · 👤 By: research foundation
🎯 Time to value: 20 minutes
📜 License: Apache-2.0 · 👤 By: research foundation
🎯 Time to value: 20 minutes
What it is: An Apache incubating project that is a local-first workspace for AI agents. It lets you run and manage agents on your own machine with data staying local. It is backed by the Apache Software Foundation. Why you'd want it: It is a privacy-first, vendor-neutral base for running agents without the cloud.
| ✓ Pros | ✗ Cons |
|---|---|
| Local-first, private by default | Very early (incubating) |
| Neutral foundation backing | Small feature set today |
| Apache-2.0 licensed | Few integrations yet |
GitHub - apache/maka: Apache Maka (Incubating) is a local-first AI agent workspace. Model messages, tool calls, tool results, permission decisions, and termination events are recorded as an append-only log.
Apache Maka (Incubating) is a local-first AI agent workspace. Model messages, tool calls, tool results, permission decisions, and termination events are recorded as an append-only log. - apache/maka
GitHub
Rank yesterday: #1 - Falling ↓
⭐ Stars today: +391 · 📦 Total: 4,385
📜 License: Not specified · 👤 By: startup
🎯 Time to value: 15 minutes
📜 License: Not specified · 👤 By: startup
🎯 Time to value: 15 minutes
What it is: The official plugin specification for the Cursor AI code editor, plus a set of first-party plugins. It defines how to extend the editor with custom capabilities. It is aimed at developers who want to build add-ons. Why you'd want it: It lets you tailor Cursor to your own workflow and share extensions.
| ✓ Pros | ✗ Cons |
|---|---|
| Official, first-party spec | Tied to one editor |
| Enables an add-on marketplace | License unclear on repo |
| Actively maintained | Early and evolving |
GitHub - cursor/plugins: Cursor plugin specification and official plugins
Cursor plugin specification and official plugins. Contribute to cursor/plugins development by creating an account on GitHub.
GitHub
Rank yesterday: not in top 6 - New entry 🆕
⭐ Stars today: +5 · 📦 Total: 21,425
📜 License: MIT · 👤 By: big-tech
🎯 Time to value: 30 minutes
📜 License: MIT · 👤 By: big-tech
🎯 Time to value: 30 minutes
What it is: A cross-platform engine for running and training machine-learning models fast. It accelerates AI across CPUs, GPUs, and specialized chips. It is a workhorse used inside many production systems. Why you'd want it: It makes models run efficiently almost anywhere without rewriting them.
| ✓ Pros | ✗ Cons |
|---|---|
| Broad hardware support | Lower-level, not plug-and-play |
| Mature and battle-tested | Setup can be fiddly |
| Backed by Microsoft | Aimed at engineers |
GitHub - microsoft/onnxruntime: ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator - microsoft/onnxruntime
GitHub
HuggingFace Trending
Top Models Today
Alibaba's open model that handles both images and text, still the most-pulled multimodal flagship.
📥 Downloads (30d): 1,726,651 · 📜 License: apache-2.0
👤 By: Alibaba (Qwen) · 🎯 Task: image-text-to-text
📐 Size: 27B
👤 By: Alibaba (Qwen) · 🎯 Task: image-text-to-text
📐 Size: 27B
What it is: A 27-billion-parameter open model that reads both pictures and text and replies in text. It is Alibaba's flagship for everyday multimodal work. Why you'd want it: A capable, freely licensed model you can run yourself for tasks that mix images and words.
| ✓ Pros | ✗ Cons |
|---|---|
| Apache-2.0, commercially usable | Large for consumer hardware |
| Strong image-and-text skills | Can over-reason on simple prompts |
| Huge download base | Needs a real graphics card (GPU) |
Qwen/Qwen3.8-27B · Hugging Face
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
The most-downloaded open model for turning an image and a prompt into video.
📥 Downloads (30d): 3,614,443 · 📜 License: MiniMax model license
👤 By: MiniMax · 🎯 Task: image-text-to-video
📐 Size: not disclosed
👤 By: MiniMax · 🎯 Task: image-text-to-video
📐 Size: not disclosed
What it is: An open model that generates short video from a still image plus a text description. It has become the default open choice for image-to-video. Why you'd want it: Free, self-hostable video generation for creators and app builders.
| ✓ Pros | ✗ Cons |
|---|---|
| Leading open video quality | Restrictive custom license |
| Massive download volume | Heavy compute to run |
| Image-plus-text control | Size not disclosed |
MiniMaxAI/MiniMax-H3 · Hugging Face
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
A 30B open multimodal model climbing on strong download volume.
📥 Downloads (30d): 505,113 · 📜 License: apache-2.0
👤 By: Meta-models community · 🎯 Task: image-text-to-text
📐 Size: 30B
👤 By: Meta-models community · 🎯 Task: image-text-to-text
📐 Size: 30B
What it is: A 30-billion-parameter open model that works with both images and text. It is trending as a freely licensed alternative to the big-lab flagships. Why you'd want it: Another Apache-licensed multimodal option you can deploy without restrictions.
| ✓ Pros | ✗ Cons |
|---|---|
| Permissive Apache-2.0 license | Newer, less proven |
| Solid multimodal ability | Large footprint |
| Rapidly growing usage | Thin documentation |
meta-models/Muse-Glimmer-30B · Hugging Face
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
DeepSeek's newest frontier-class text model under a clean MIT license.
📥 Downloads (30d): 49,601 · 📜 License: mit
👤 By: DeepSeek · 🎯 Task: text-generation
📐 Size: frontier-class (undisclosed)
👤 By: DeepSeek · 🎯 Task: text-generation
📐 Size: frontier-class (undisclosed)
What it is: DeepSeek's latest high-end text model, released under the permissive MIT license. It targets demanding reasoning and coding work. Why you'd want it: Frontier-level text ability with unusually few licensing strings attached.
| ✓ Pros | ✗ Cons |
|---|---|
| MIT license is very permissive | Very large to self-host |
| Strong reasoning and coding | Exact size undisclosed |
| From a top open-model lab | Needs serious hardware |
deepseek-ai/DeepSeek-V4-Pro-0813 · Hugging Face
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
A giant mixture-of-experts model - 2.4 trillion parameters, but only ~95B active per query.
📥 Downloads (30d): 15,702 · 📜 License: Qwen license
👤 By: Alibaba (Qwen) · 🎯 Task: text-generation
📐 Size: 2.4T total / 95B active
👤 By: Alibaba (Qwen) · 🎯 Task: text-generation
📐 Size: 2.4T total / 95B active
What it is: A very large model that uses a mixture-of-experts design, so only a fraction of it (about 95 billion parameters) runs on any given query. That keeps a huge model relatively affordable to run. It is aimed at top-end reasoning. Why you'd want it: Frontier-scale capability with running costs closer to a mid-size model.
| ✓ Pros | ✗ Cons |
|---|---|
| Huge capacity, efficient serving | Enormous to host at all |
| Only part activates per query | Custom Qwen license |
| Strong on hard tasks | Impractical for individuals |
Qwen/Qwen3.8-2.4T-A95B · Hugging Face
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
A fast open model for turning a still image into video.
📥 Downloads (30d): 654,175 · 📜 License: Lightricks license
👤 By: Lightricks · 🎯 Task: image-to-video
📐 Size: not disclosed
👤 By: Lightricks · 🎯 Task: image-to-video
📐 Size: not disclosed
What it is: An open image-to-video model known for speed. It is widely used for quick clip generation. It comes from the maker of several consumer creative apps. Why you'd want it: Faster video generation when you need many clips without long waits.
| ✓ Pros | ✗ Cons |
|---|---|
| Fast generation | Custom license terms |
| Heavily used | Quality trails the top model |
| Good for high volume | Size not disclosed |
Lightricks/LTX-2.5 · Hugging Face
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
An open model that generates music from a text description.
📥 Downloads (30d): 15,678 · 📜 License: MiniMax license
👤 By: MiniMax · 🎯 Task: text-to-audio
📐 Size: not disclosed
👤 By: MiniMax · 🎯 Task: text-to-audio
📐 Size: not disclosed
What it is: An open model that turns a text prompt into music. It is part of MiniMax's growing family of generative media models. It targets creators who need original background tracks. Why you'd want it: Royalty-free, self-generated music for videos and apps.
| ✓ Pros | ✗ Cons |
|---|---|
| Free music generation | Custom license |
| From an active media lab | Quality varies by genre |
| Self-hostable | Size not disclosed |
MiniMaxAI/MiniMax-Music3 · Hugging Face
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
A mixture-of-experts text model that stays cheap to run - 35B total, only 3B active.
📥 Downloads (30d): 9,165 · 📜 License: mit
👤 By: Ornith AI · 🎯 Task: text-generation
📐 Size: 35B total / 3B active
👤 By: Ornith AI · 🎯 Task: text-generation
📐 Size: 35B total / 3B active
What it is: A text model using a mixture-of-experts design where only 3 billion of its 35 billion parameters run per query. That makes it efficient for its capability. It is trending on the strength of that efficiency. Why you'd want it: Good text quality at running costs close to a small model.
| ✓ Pros | ✗ Cons |
|---|---|
| Efficient mixture-of-experts design | Newer, less proven |
| MIT-licensed | Smaller community |
| Cheap to serve | Limited track record |
ornith-ai/Ornith-1.5-35B-A3B · Hugging Face
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
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Google Antigravity: Run and monitor several coding agents at once in an IDE | Product Hunt
Google Antigravity - Build the new way
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🔥 Upvotes: not disclosed · 👤 By: ShogunAI
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API Pricing
Snapshot
| Provider | Model | Input $/1M | Output $/1M | Context |
|---|---|---|---|---|
| Anthropic | Claude Opus (4.8 / 5 class) | $5.00 | $25.00 | 200K (1M available) |
| Anthropic | Claude Sonnet (4.6 / 5 class) | $2.00 | $10.00 | 200K (1M available) |
| OpenAI | GPT-5.6-terra | $2.00 | $12.00 | ~400K |
| Gemini 2.5 Pro | $1.25 | $10.00 | 1M | |
| Groq | Llama 3.3 70B (LPU) | $0.59 | $0.79 | 128K |
Prices are holding steady from yesterday, with no changes flagged across the major providers. Anthropic's Sonnet rate of $2/$10 was made permanent on August 11, and a planned September increase was cancelled.
What this means: Frontier quality keeps getting cheaper - Opus at $5/$25 and OpenAI's flagship tier at $2/$12 now sit near where mid-tier models were a year ago. For high-volume work that does not need deep reasoning, Groq's open-weight Llama 70B under $0.80 per million output tokens is roughly ten times cheaper. Match the model tier to the task instead of defaulting to the flagship.
What this means: Frontier quality keeps getting cheaper - Opus at $5/$25 and OpenAI's flagship tier at $2/$12 now sit near where mid-tier models were a year ago. For high-volume work that does not need deep reasoning, Groq's open-weight Llama 70B under $0.80 per million output tokens is roughly ten times cheaper. Match the model tier to the task instead of defaulting to the flagship.
arXiv Paper of the Day
Learning When to Think: Adaptive Reasoning for Test-Time Compute Allocation
Gijs Kassenaar, Zhao Yang, Vincent Francois-Lavet · arXiv:2608.20256
What it claims: Instead of spending the same amount of "thinking" on every question, the model learns to pick one of three modes as its very first output - quick answer, brief reasoning, or extended reasoning. The routing is trained into the model itself, with no separate router.
Key finding: On grade-school math it cut token use by 76%, and on harder math it held accuracy (0.782 vs 0.796) while cutting average response length 41%.
Why practitioners should care: Thinking tokens are a direct line on your inference bill. Letting the model reason at length only on genuinely hard problems can roughly halve token spend without meaningfully hurting accuracy.
Key finding: On grade-school math it cut token use by 76%, and on harder math it held accuracy (0.782 vs 0.796) while cutting average response length 41%.
Why practitioners should care: Thinking tokens are a direct line on your inference bill. Letting the model reason at length only on genuinely hard problems can roughly halve token spend without meaningfully hurting accuracy.
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