GenAI Secret Sauce Daily Digest

By the Numbers

Statistically Speaking

77.6% on SWE

Mistral Bets on Dense

Top Story

$1.50 per million input tokens, $7

Mistral Bets on Dense

77.6% on SWE-Bench Verified

Mistral Bets on Dense

$1.50 per million input tokens, $7.50 output

Mistral Bets on Dense

$0.14 per million input tokens, $0

DeepSeek V4 Flash

90% reduction in KV cache memory

DeepSeek V4 Flash

One Thing to Tell Your Friends

A 732-byte Python script can take full control of any Linux computer shipped since 2017 - and the bug was hiding in the kernel's own security system for nine years.

Summary

TL;DR

Hot off the Presses

01

Mistral Bets on Dense: Medium 3.5 Is 128 Billion Parameters With No Shortcuts

What this means for you: If you use AI coding tools at work, a French company just proved that the dominant approach to building large AI models may be unnecessarily complicated - and their simpler alternative matches the best coding AI available.

While nearly every AI lab in 2026 builds models using Mixture-of-Experts (MoE) - a design where only a fraction of the model activates per query - Mistral went the opposite direction. Medium 3.5 is a "dense" model where all 128 billion parameters work on every request.

Mistral also launched "Vibe," a cloud-based coding agent service where Medium 3.5 runs tasks asynchronously and notifies developers when done - moving AI coding from "copilot" to "colleague you check in on."

77.6% on SWE-Bench Verified - the industry's standard test for AI coding agents, matching or exceeding most MoE competitors
256,000-token context window - enough to process an entire medium-sized codebase in one go
$1.50 per million input tokens, $7.50 output - roughly half the price of Claude Sonnet
Configurable reasoning effort - developers can toggle between fast responses and deep thinking mode per request
Vision support built in - the model can read screenshots, diagrams, and UI mockups alongside code

77.6%

on SWE

$1.50

per million input tokens, $7

Mistral announcement →Model on HuggingFace →

02

A 732-Byte Script Can Root Any Linux Machine Shipped Since 2017

What this means for you: If your company runs servers, cloud infrastructure, or anything on Linux - which includes most of the internet - there is a critical security patch you need to apply immediately.

CVE-2026-31431 is a privilege escalation vulnerability in the Linux kernel's cryptographic authentication system that has existed since 2017. Unlike most kernel exploits that require precise timing or version-specific tricks, this one is a "straight-line logic flaw" - a simple mistake in how the kernel checks permissions.

“732 bytes. That's all it takes to own any Linux box shipped in the last nine years.”

Every mainstream Linux distribution since 2017 is affected - Ubuntu, Debian, Red Hat, SUSE, Arch, all of them
A 732-byte Python script is the entire exploit - no special tools, no network access, no race conditions
Only requires an unprivileged local user account - any employee, contractor, or compromised service account is enough
The attack chains through the kernel's own crypto subsystem - ironic because the vulnerability lives in code designed to verify authenticity

copy.fail (full disclosure) →

03

DeepSeek V4 Flash: One Million Tokens of Context for 14 Cents

What this means for you: The cost of asking an AI to read and analyze a long document just dropped by roughly 95% compared to leading competitors - making AI analysis of legal contracts, codebases, and research papers dramatically more accessible.

DeepSeek V4 Flash is a new variant of DeepSeek's V4 model family (V4 Pro covered April 24) optimized for speed and cost. It uses a Mixture-of-Experts design where only 13 billion of its 284 billion total parameters activate per query.

The price point puts frontier-class AI analysis within reach of individual developers and small businesses for the first time.

$0.14 per million input tokens, $0.28 output - roughly 20x cheaper than Claude Sonnet and 14x cheaper than GPT-4.1
1,048,576-token context window - enough to process a 750-page book in one request
Three architectural innovations - hybrid attention for reduced memory, enhanced residual connections, and the Muon optimizer for training stability
90% reduction in KV cache memory - a technical improvement that means the model can handle longer inputs without running out of Graphics Processing Unit (GPU) memory

$0.14

per million input tokens, $0

90%

reduction in KV cache memory

DeepSeek V4 Flash on DeepInfra →

04

Ramp's AI Spreadsheet Was Leaking Financial Data Through Hidden Commands

What this means for you: If your company uses any AI-powered spreadsheet or data tool that processes imported data, that data could contain invisible instructions that tell the AI to send your financial information to an attacker.

PromptArmor disclosed a vulnerability in Ramp's Sheets AI - the AI agent built into the corporate expense management platform used by thousands of companies. The attack works through "indirect prompt injection": malicious instructions hidden in imported data that the AI follows without the user's knowledge.

“The AI didn't just read the financial data - it packaged it up and mailed it to the attacker, disguised as an image request.”

No user approval required - the attack triggers automatically when the spreadsheet renders
Financial data exfiltrated via IMAGE formulas - the AI was tricked into embedding sensitive data in image URLs that sent network requests to attacker-controlled servers
The attack surface is any imported data - CSV files, copied cells, or API-connected data sources could all carry the payload
Ramp has patched the specific vulnerability - but the attack pattern applies to any AI tool that processes untrusted data

PromptArmor disclosure →

05

An NVIDIA Executive Admits AI Costs More Than the Humans It Replaces

What this means for you: The company that sells more AI hardware than anyone else just said the quiet part out loud - right now, for many teams, AI infrastructure is more expensive than the people it's supposed to help or replace.

An NVIDIA vice president publicly stated that compute costs for his team "far exceed" employee costs. This is striking because NVIDIA's entire business depends on companies believing AI saves money.

The disconnect between AI's promise and its current economics is becoming harder to ignore as companies report quarterly earnings.

Tech layoffs have exceeded 92,000 in 2026 - including 8,000+ at Meta and 6,000 unfilled positions at Microsoft
A 2024 MIT study found AI automation is economically viable in only 23% of vision-focused roles - 77% remain cheaper to do with humans
Companies are cutting headcount and increasing AI spending simultaneously - but the math doesn't always work in their favor
The admission came during a discussion about AI infrastructure investment - suggesting even NVIDIA recognizes the cost gap hasn't closed yet

Fortune →

Trends & Themes

The Agentic Tooling Gold Rush Is Rewriting How Developers Work

Why this matters to you: The tools developers use to manage AI coding assistants are evolving faster than the AI models themselves - and the best ones are free and open-source.

The pattern: AI coding tools are becoming platforms, and the community is building the plugin ecosystem faster than the companies can.

Warp terminal went open-source and gained 11,955 GitHub stars in a single day - it's now an "agentic development environment" that orchestrates Claude Code, Codex, and Gemini CLI sessions simultaneously
Superpowers framework hit 173,000 total stars - it enforces brainstorm-design-plan-test workflows before AI agents write code, making long autonomous runs more reliable
Craft Agents (Apache 2.0) launched as a desktop app - built on the Claude Agent SDK, it gives non-technical users a GUI for running multiple AI agents with Gmail, Linear, and Slack integrations
59 open-source Claude skills and a curated Codex skills library both launched this week - community-built integrations that extend AI coding tools to send emails, file issues, and post to Slack

AI Evaluation Is Becoming Unsustainably Expensive

Why this matters to you: The tests that companies use to prove their AI is better than the competition now cost more than most teams can afford to run - which means the benchmarks you see in marketing materials may be increasingly unreliable.

The HuggingFace EvalEval Coalition documented these numbers and warned that evaluation costs are approaching training costs for some models - a threshold that could slow the pace of AI improvement.

Running the HAL agent benchmark costs $40,000 for a single evaluation across 21,730 test scenarios
SWE-bench Verified ranges from $4 to $1,600 per agent - a 400x cost spread depending on the model and configuration
PaperBench costs approximately $9,500 per evaluation - just to test whether an AI can replicate research papers
Adding reliability reruns multiplies costs 8x - turning a $1,600 test into a $12,800 one
Static benchmarks can be compressed 100-200x, but agent benchmarks achieve only 2-3.5x compression - the fundamental economics are different

HuggingFace blog →

Dense Models Challenge the Mixture-of-Experts Consensus

Why this matters to you: The AI industry's dominant architecture choice - Mixture-of-Experts, where only part of the model runs per query - may not be the only path to powerful AI, which could lead to more diverse and competitive models.

Mistral's bet suggests the "all-MoE-all-the-time" approach may be premature. If dense models at 128B can match MoE models at 1T+, the optimal architecture is still an open question.

Mistral Medium 3.5 uses all 128B parameters on every query - a "dense" approach most labs abandoned in favor of MoE, yet it matches MoE models on SWE-bench
IBM Granite 4.1 8B matches its own 32B predecessor - showing that smaller, denser models can match larger ones with better training
Meanwhile, MoE models keep getting bigger - DeepSeek V4 has 1.6 trillion total parameters, Kimi K2.6 has 1.1 trillion, but both activate only 13-32 billion per query
The trade-off is cost vs. simplicity - MoE models are cheaper to run (fewer active parameters) but harder to train, deploy, and fine-tune

Prompt Injection Moves From Research to Boardroom Risk

Why this matters to you: The technique that made Ramp's AI leak financial data is not a one-off bug - it's a fundamental weakness in how AI tools process information, and researchers are finding it everywhere.

Three separate sources today - a real-world exploit, a defense tool, and academic research - converge on the same conclusion: AI tools that process untrusted data are fundamentally vulnerable, and we don't have a complete solution yet.

Ramp's Sheets AI exfiltrated financial data through hidden instructions in imported spreadsheet cells - no user action required
Arc Gate, a new prompt injection defense proxy, achieves F1 score of 0.947 - impressive but still misses 5% of attacks, which is too many for financial data
A comprehensive research paper found that every model-based prompt injection defense eventually breaks - only application-layer output filtering (checking what the AI sends back) reliably works
PARASITE, a new attack technique, poisons system prompts - the instructions that tell AI how to behave can themselves be compromised

The AI Workforce Conversation Gets Specific Numbers

Why this matters to you: The debate about AI replacing jobs moved from speculation to data this week, and the numbers are more nuanced than either optimists or pessimists claim.

The emerging picture: AI is displacing some entry-level roles while simultaneously costing more than expected for companies that adopt it. The people most at risk are those whose work is easy to evaluate but expensive to produce - exactly the profile of junior creative freelancers.

452 upvotes on r/ClaudeAI for "the final nail in the coffin for entry-level creative freelancers" - the most-upvoted thread of the day reflects widespread anxiety
NVIDIA's own executive says compute costs exceed employee costs - undermining the pure cost-replacement argument
92,000 tech layoffs in 2026 so far - outpacing 2025's full-year total of approximately 120,000
Only 23% of vision-focused roles are economically viable to automate according to MIT research - 77% remain cheaper with humans
The Pragmatic Engineer podcast highlighted "vibe slop" - code quality declining across 30+ engineering teams as developers review AI output less carefully

Creative AI & Media

Tencent's HY-World 2.0 Converts Photos Into Real 3D Scenes

What this means for you: You can now take photos of a room, a building, or a landscape and get back actual 3D models you can import into game engines, VR apps, or 3D design tools - not just a video.

Four-stage pipeline - panorama generation, trajectory planning, world expansion, and world composition
Outputs real 3D assets - meshes and 3D Gaussian Splattings that work in Blender, Unreal Engine, and NVIDIA Isaac Sim
Runs on consumer GPUs with real-time rendering
Community license from Tencent

HuggingFace →

Cursor Camp: A Whimsical Multiplayer Web Experiment

What this means for you: The creator of viral web experiences like Stimulation Clicker made a new one where cursors from different visitors share a virtual space - and it hit 484 points on Hacker News.

Club Penguin-like aesthetics with hidden interactions and a discoverable treehouse area
Pure creative web art - no AI generation, just clever interaction design
From Neal Agarwal (neal.fun) - known for viral interactive experiences

Cursor Camp →

AI Playtesting Finds Bugs in 12 Minutes That Would Take Hours by Hand

What this means for you: Game developers can now use AI agents to playtest their games automatically - one developer completed testing of 5 new features in 12 minutes using about 120,000 tokens.

Node.js wrapper creates a text interface for the AI without modifying game code
The AI played through a roguelike dungeon game and found edge cases a human tester would need 30+ minutes of additional testing to cover
Cost: roughly $0.50 per testing session at current API prices

Blog post →

Developer Tools

Developer Tools & Infrastructure

Simon Willison's LLM (Large Language Model) Library Gets Its Biggest Refactor

What this means for you: The most popular open-source command-line tool for working with AI models just learned to handle images, audio, video, structured data, and tool calls - not just text.

LLM 0.32a0 evolves from prompt-response to full multimodal support
Typed streaming parts - responses now stream as distinct events (text, tool calls, reasoning) rather than undifferentiated text
New serialization methods for saving and loading complete conversations
Backwards-compatible - existing plugins and workflows continue to work

Simon Willison's blog →

llama.cpp Merges Native Blackwell NVFP4 Support

What this means for you: If you run AI models on your own NVIDIA graphics card, the most popular local inference engine just got significantly faster on the newest hardware.

PR #22196 merged - native NVFP4 quantization for NVIDIA's Blackwell architecture (RTX 50-series)
Community immediately released NVFP4 quantized models for Gemma 4 31B, Nemotron Cascade 2, and Qwen 3.5 35B
ik_llama.cpp added Qwen 3.5/3.6 MTP speculative decoding - a technique that boosts throughput by approximately 30%
Dual RTX 5060 Ti 16GB setup running Qwen3.6 27B at 60 tokens per second with 204K context - reported by a community member

llama.cpp PR →

Warp Terminal Goes Open-Source

What this means for you: A popular Rust-based terminal that has evolved into an AI agent management environment is now free and open-source, gaining nearly 12,000 GitHub stars in one day.

Dual-licensed AGPL-3.0 / MIT - the UI framework is MIT, the core is AGPL
Natively integrates with Claude Code, Codex, and Gemini CLI as orchestrated agents
Agent management dashboards for tracking thousands of concurrent AI sessions
Built in Rust for performance

GitHub →

Research & Models

IBM Granite 4.1: An Enterprise AI Family That Punches Above Its Weight

What this means for you: IBM released a complete set of AI models - language, vision, speech, embedding, and safety - designed for businesses that need reliable, well-documented AI they can run on their own servers.

Granite 4.1 8B matches the previous Granite 4.0 32B - a 4x efficiency improvement
Available at 3B, 8B, and 30B parameter sizes - trained on approximately 15 trillion tokens
Context lengths up to 512,000 tokens - enough for large document processing
Granite Speech 4.1 2B achieves competitive transcription accuracy at a fraction of the size of alternatives
Apache 2.0 license - fully open for commercial use

IBM Research blog →HuggingFace →

Xiaomi's MiMo-V2.5-Pro Beats Claude on Coding - Under MIT License

What this means for you: A smartphone company from China just released a free, open-source AI model that outperforms one of the most expensive commercial AI services on coding tasks.

57.2% on SWE-bench - surpassing Claude Opus 4.6 (53.4%) and approaching GPT-5.4 (57.7%)
1.02-trillion parameter MoE with 42B active and a 1-million-token context window
MIT license - no restrictions on commercial use
40-60% fewer tokens consumed than comparable competitors on ClawEval
Demonstrated building a complete Rust compiler and functional web browsers in agentic testing

r/LocalLLaMA discussion →

NVIDIA Nemotron Omni: One Model for Video, Audio, Images, and Text

Previously: April 28 - NVIDIA launched a multimodal AI model that runs on a single chip.

Today: Community reception confirms this is one of the most versatile open-weight models available. The 30B-parameter Mamba2-Transformer hybrid accepts up to 2 minutes of video, 1 hour of audio, images, and text in a single forward pass. Available in FP8 (32.8 GB) and NVFP4 (20.9 GB) quantizations for consumer hardware deployment.

HuggingFace →

Business & Industry

NVIDIA: AI Infrastructure Costs Exceed Employee Costs

An NVIDIA VP stated compute costs "far exceed" employee costs for his team - a remarkable admission from the company selling the hardware
Raises questions about the enterprise AI ROI narrative - if the biggest AI hardware vendor's own teams find it expensive, smaller companies face steeper economics
Context: NVIDIA's data center revenue exceeded $130 billion annualized in recent quarters, suggesting the spend is very real

Fortune →

Snapchat Embeds AI Ads Directly Into Chat Conversations

AI Sponsored Snaps launched April 28 - AI agents guide users from product discovery through purchase within a single chat thread
950 billion chats processed in Q1 2026 - the scale of the advertising surface is enormous
500 million users have interacted with My AI since launch; nearly 1 billion monthly active users will see the ads
The AI makes ads "feel like conversation" rather than interruptive banners - raising questions about disclosure and consent

Interesting Engineering →

Nous Research Announces Hermes Agent Platform

AMA on r/LocalLLaMA drew 239 upvotes - significant community interest
Hermes Agent is a new platform from the team behind the popular Hermes fine-tuned models
Focused on open-source agentic AI with community-driven development

Hermes Agent →Reddit AMA →

IBM Plans 750 AI and Quantum Jobs in Chicago

New hub focused on AI and quantum computing research and development
Part of IBM's broader enterprise AI push following the Granite 4.1 launch

LinkedIn News →

Education

GenAI in Education

AI Cheating and Academic Integrity Dominate r/Professors

Why this matters to you: If you work in education or have students in your life, today's faculty discussions reveal how widespread AI cheating has become - and how institutions are struggling to respond.

The pattern across 15+ threads today: faculty feel caught between students who view AI use as normal, administrators who want to avoid conflict, and an academic system that hasn't adapted its assessment methods to a world where AI can produce passable work in seconds.

"Graded final essays today" (183 upvotes) - a professor reports widespread AI-generated submissions in final exams, raising questions about whether traditional essay assignments remain viable
"CAUGHT a student using ChatGPT to do Homework IN CLASS" (38 upvotes) - describing a student openly using AI during supervised work, suggesting normalization of AI assistance
"A dean has been pushing the academic integrity board to be less 'mean'" (39 upvotes) - institutional pressure to soften consequences for AI cheating, undermining faculty enforcement
"Will smaller universities survive an education recession?" (30 upvotes) - broader concerns about enrollment declines intersecting with AI disruption

Surprising

Surprising & Under-the-Radar

PS5s Can Now Run Linux - and the Local AI Community Noticed

A public jailbreak for the PlayStation 5 means the console's powerful GPU could theoretically run local AI models. At 94 upvotes on r/LocalLLaMA, the community is already speculating about inference performance.

Tom's Hardware →

"Vibe Slop" Is Shipping in Serious Engineering Teams

The Pragmatic Engineer podcast featured Flask creator Armin Ronacher warning that code quality is declining across 30+ engineering teams he's observed, with AI-generated "vibe slop" making it past code review because developers are reviewing AI output less carefully.

Pragmatic Engineer →

Why Isn't LLM Reasoning Done in Vector Space Instead of Natural Language?

A 358-upvote r/LocalLLaMA discussion asks a deceptively simple question: if AI models think internally in high-dimensional vectors, why do we force them to "reason" by generating English words? The thread surfaced multiple research directions exploring latent-space reasoning that could be far more efficient.

Opus 4.7 User Frustration Continues

Previously: April 27 - GPT-5.5's System Card revealed spontaneous persuasion; April 17 - Opus 4.7 hidden cost analysis.

Today: "Opus 4.7 is just 4.6 with a stick up its butt" hit 162 upvotes on r/ClaudeAI, with users reporting the model feels more restrictive without meaningful capability improvements. Meanwhile, a separate 85-upvote thread titled "AI is making me less productive and more distracted" echoes the AI dependency research from April 20.

A Self-Modifying AI Agent Exists Because Claude Code Got Too Unpredictable

Mario Zechner built Pi, a self-modifying coding agent, specifically because Claude Code became unpredictable as features accumulated. The agent can rewrite its own behavior based on what it learns during a session - a fundamentally different approach to AI-assisted development.

Worth Watching

Signals to Track

01

Microsoft VibeVoice: Open-Source Voice AI That Handles 90-Minute Conversations

Why this is worth watching right now: Microsoft just MIT-licensed frontier voice AI that rivals commercial APIs costing thousands per month.

VibeVoice includes three models: ASR for 60-minute transcription with speaker identification, TTS for generating 90 minutes of multi-speaker audio, and a streaming model for real-time synthesis. All MIT-licensed, all production-ready. This could reshape the economics of voice AI applications, from podcast production to call center analytics. If adoption scales, commercial voice API pricing will face serious pressure.

GitHub →

02

AI Evaluation Costs Are Approaching Training Costs

Why this is worth watching right now: the economics of proving your AI is good may become more expensive than making it good.

The HuggingFace EvalEval Coalition's numbers are sobering: $40,000 for a single benchmark run, with agent evaluations resistant to the compression techniques that make static benchmarks affordable. If this trend continues, only the largest labs will be able to credibly evaluate their models - creating an information asymmetry where smaller companies can't verify (or dispute) benchmark claims.

03

Google Deep Research Max Enters the Autonomous Agent Race

Why this is worth watching right now: Google's research agent scores 77.1% on ARC-AGI-2, more than double its predecessor.

Launched on April 21 with Gemini 3.1 Pro under the hood, Deep Research Max uses extended test-time compute and Model Context Protocol (MCP) for connecting to private enterprise data. Native visualization generation and multi-tool orchestration position it as a serious competitor to Anthropic's and OpenAI's agent offerings. The ARC-AGI-2 score is particularly notable - this benchmark was designed to resist brute-force scaling.

04

CodeHealth MCP Server: Deterministic Feedback for AI-Generated Code

Why this is worth watching right now: it addresses AI code quality without using another AI as the judge.

CodeScene's MCP server plugs deterministic code-health analysis directly into AI coding agents, creating a self-correcting feedback loop based on years of research into technical debt metrics. Unlike "LLM-as-judge" approaches, the analysis is repeatable and explainable. If agentic coding continues to grow, tools that provide non-AI quality gates could become essential.

05

Plurai: "Vibe-Training" Evals Without Labeled Data

Why this is worth watching right now: if small models can reliably evaluate large models, the $40,000 benchmark problem gets much cheaper.

Plurai's approach uses small language models tuned via "vibe-training" from plain-language task descriptions - no annotation pipelines needed. Claims: sub-100ms latency, 8x lower cost than GPT-as-judge, 43% fewer failures. If these numbers hold under independent testing, this could democratize AI evaluation.

GitHub Trending

Top Repos Today

#1

warpdotdev/warp

Rank yesterday: New entry 🆕

⭐ Stars today: +11,955 · 📦 Total: 43.4k
📜 License: AGPL-3.0 / MIT (dual) · 👤 By: company
🎯 Time to value: 5 minutes

What it is: Warp is a Rust-based terminal that has evolved into an agentic development environment. It natively integrates with Claude Code, Codex, and Gemini CLI, letting developers orchestrate multiple AI agent sessions from a single interface. The UI framework is MIT-licensed while the core engine is AGPL-3.0. Why you'd want it: If you're juggling multiple AI coding agents, Warp provides a purpose-built environment with session tracking, agent management dashboards, and concurrent agent support - rather than running everything in separate terminal tabs.

✓ Pros	✗ Cons
Native multi-agent orchestration	AGPL core limits commercial embedding
Rust performance, polished UI	Learning curve vs. standard terminals
Active development, massive community	Resource-heavier than minimal terminals

#2

microsoft/VibeVoice

Rank yesterday: New entry 🆕

⭐ Stars today: +1,688 · 📦 Total: 45.6k
📜 License: MIT · 👤 By: company (Microsoft Research)
🎯 Time to value: 15 minutes

What it is: A suite of three open-source voice AI models: ASR for long-form transcription with speaker diarization, TTS for generating up to 90 minutes of multi-speaker audio, and a 0.5B streaming model for real-time synthesis. Built on continuous speech tokenizers and diffusion-based generation integrated with language models. Why you'd want it: MIT-licensed alternative to commercial voice APIs like ElevenLabs or Whisper, handling speaker-aware transcription and hour-long audio generation with no usage fees.

✓ Pros	✗ Cons
MIT license, no usage fees	Requires GPU for real-time streaming
60-min ASR with speaker ID	Limited non-English language support
Production-ready from Microsoft Research	Large model sizes for TTS

#3

obra/superpowers

Rank yesterday: New entry 🆕

⭐ Stars today: +1,683 · 📦 Total: 173k
📜 License: MIT · 👤 By: individual
🎯 Time to value: 10 minutes

What it is: A methodology framework that forces AI coding agents to follow brainstorm-design-plan-TDD workflows before writing code. It works with any agent (Claude Code, Codex, etc.) and makes long autonomous runs more reliable by requiring approved specs before implementation. Why you'd want it: If your AI coding agent goes off-rails or skips planning, Superpowers enforces the discipline a senior engineer would apply - making autonomous sessions reviewable and reliable.

✓ Pros	✗ Cons
Works with any AI coding agent	Adds overhead to quick tasks
Enforces TDD and design review	Methodology requires buy-in
173k stars, battle-tested	Opinionated workflow may not fit all teams

#4

ComposioHQ/awesome-codex-skills

Rank yesterday: New entry 🆕

⭐ Stars today: +1,180 · 📦 Total: 4.7k
📜 License: Not specified · 👤 By: company
🎯 Time to value: 5 minutes

What it is: A curated collection of skill files for OpenAI's Codex CLI that enable the agent to automate workflows across 1,000+ external services - GitHub, Slack, email, Linear, and more. Skills auto-trigger based on what you ask. Why you'd want it: Drop-in integrations that let your Codex agent send emails, file issues, post to Slack, and manage PRs without building custom tooling.

✓ Pros	✗ Cons
1,000+ service integrations	No explicit license file
Auto-triggering based on context	Codex CLI-specific (not portable)
Community-maintained, growing fast	Quality varies across skills

#5

abhigyanpatwari/GitNexus

Rank yesterday: New entry 🆕

⭐ Stars today: +777 · 📦 Total: 33.3k
📜 License: PolyForm Noncommercial 1.0.0 · 👤 By: individual
🎯 Time to value: 10 minutes

What it is: A client-side code intelligence engine that builds interactive knowledge graphs of codebases using Tree-sitter parsing, Graph RAG (Retrieval-Augmented Generation), and hybrid BM25 + semantic search. Nothing leaves your machine - all processing happens locally. Why you'd want it: Gives AI coding agents true codebase-wide understanding through MCP integration with Cursor and Claude Code, without uploading source code to any server.

✓ Pros	✗ Cons
Fully local, privacy-preserving	Noncommercial license
14+ language support	Initial indexing can be slow on large repos
MCP integration with Cursor, Claude Code	Browser-based UI still maturing

#6

lukilabs/craft-agents-oss

Rank yesterday: New entry 🆕

⭐ Stars today: +432 · 📦 Total: 5.3k
📜 License: Apache-2.0 · 👤 By: company (Craft.do)
🎯 Time to value: 5 minutes

What it is: A desktop app from the Craft.do team that wraps the Claude Agent SDK in a polished UI for running multiple AI agents simultaneously. Connect agents to Gmail, Linear, Slack, and other services through a visual interface without writing configuration files. Why you'd want it: Production-ready GUI for AI agent workflows - aimed at knowledge workers who want agent power without CLI friction.

✓ Pros	✗ Cons
Apache 2.0, fully open	macOS-first (limited cross-platform)
Visual multi-agent management	Requires Claude API key
Built by active product team	Smaller community than CLI tools

#7

CJackHwang/ds2api

Rank yesterday: New entry 🆕

⭐ Stars today: +461 · 📦 Total: 2.7k
📜 License: AGPL-3.0 · 👤 By: individual
🎯 Time to value: 15 minutes

What it is: A Go middleware that converts DeepSeek's web interface into OpenAI, Claude, and Gemini-compatible API endpoints. Includes multi-account rotation to avoid rate limits and a React management dashboard. Why you'd want it: Use DeepSeek's models through existing OpenAI-compatible tools without paying API costs - though the web-scraping nature means it's best suited for personal or experimental use.

✓ Pros	✗ Cons
Free access to DeepSeek models	Web scraping may violate ToS
Compatible with all major SDKs	AGPL license limits commercial use
Multi-account rotation	Depends on DeepSeek's web UI stability

#8

ZhuLinsen/daily_stock_analysis

Rank yesterday: New entry 🆕

⭐ Stars today: +358 · 📦 Total: 32.7k
📜 License: MIT · 👤 By: individual
🎯 Time to value: 20 minutes

What it is: An LLM-powered system that automatically analyzes stocks across A-share, Hong Kong, and US markets daily, combining technical analysis, fundamentals, and news sentiment into decision dashboards with buy/sell signals. Runs free via GitHub Actions. Why you'd want it: Zero-infrastructure, free-to-run AI stock analyst covering three major markets - one of the few tools that handles Chinese A-shares and US equities together with automated LLM commentary.

✓ Pros	✗ Cons
Free via GitHub Actions	AI-generated financial advice carries risk
Three-market coverage (CN/HK/US)	Primarily Chinese-language interface
Multi-channel notifications	Backtesting results may not reflect live performance

HuggingFace Trending

Top Models Today

#1

deepseek-ai/DeepSeek-V4-Pro

State-of-the-art MoE model with 862B active parameters, 1M-token context, and 27% fewer FLOPs than its predecessor.

📥 Downloads (30d): 174K · 📜 License: MIT
👤 By: DeepSeek AI · 🎯 Task: text-generation
📐 Size: 1.6T total (49B activated)

What it is: DeepSeek's flagship model, pre-trained on 32T+ tokens, supporting three thinking modes (Non-think, Think High, Think Max) and a million-token context window using novel Compressed Sparse Attention. Why you'd want it: The most capable open-weight model available for complex coding, long-document analysis, and advanced reasoning - MIT licensed with no usage restrictions.

✓ Pros	✗ Cons
MIT license, frontier performance	Requires significant infrastructure
Three thinking modes	1.6T total params = expensive to host
1M-token context	Limited fine-tuning ecosystem so far

#2

moonshotai/Kimi-K2.6

Open-source agentic MoE model with 300-subagent swarm capability and 80.2% SWE-bench Verified.

📥 Downloads (30d): 489K · 📜 License: Modified MIT
👤 By: Moonshot AI · 🎯 Task: image-text-to-text
📐 Size: 1.1T total (32B activated)

What it is: A multimodal MoE model with 256K context that can orchestrate up to 300 sub-agents executing 4,000 coordinated steps. Accepts text, images, and video. Why you'd want it: One of the strongest open-source models for building autonomous coding agents or multi-agent pipelines, with native swarm orchestration built in.

✓ Pros	✗ Cons
80.2% SWE-bench, 96.4% AIME 2026	Modified MIT has some restrictions
300-agent swarm capability	Very large model footprint
Multimodal (text + image + video)	Newer, smaller community than DeepSeek

#3

Qwen/Qwen3.6-35B-A3B

Alibaba's MoE vision-language model with only 3B active parameters - the most downloaded trending model at 1.51M downloads.

📥 Downloads (30d): 1.51M · 📜 License: Apache 2.0
👤 By: Alibaba Qwen Team · 🎯 Task: image-text-to-text
📐 Size: 36B total (3B activated)

What it is: A MoE model that activates only 3B parameters per token, supporting 256K native context (extensible to 1M), multimodal inputs including hour-long video, and native tool/MCP integration. Why you'd want it: Extremely low inference cost (3B active parameters) makes it deployable on modest hardware while still delivering 73.4% on SWE-bench Verified - under Apache 2.0.

✓ Pros	✗ Cons
1.51M downloads, proven by community	3B active may limit complex reasoning
Apache 2.0, runs on consumer GPUs	Video processing needs more VRAM
Native MCP and tool integration	Newer than Qwen3.5, ecosystem catching up

#4

google/gemma-4-31B-it

Google's open multimodal model with 6.56M downloads - the most downloaded model on this list.

📥 Downloads (30d): 6.56M · 📜 License: Apache 2.0
👤 By: Google DeepMind · 🎯 Task: image-text-to-text
📐 Size: 33B

What it is: A 31B dense multimodal model with hybrid local+global attention, variable image resolution, built-in thinking mode, and support for 140+ languages and function calling. Why you'd want it: The go-to choice for production multimodal applications - Apache 2.0, massive download numbers proving stability, and Google's backing for enterprise trust.

✓ Pros	✗ Cons
6.56M downloads, production-proven	Dense 33B = more compute than MoE
140+ languages, Apache 2.0	Chat template bugs found in llama.cpp
Built-in thinking and function calling	No native agentic features

#5

openai/privacy-filter

OpenAI's first open PII detector - a sparse MoE model that runs in browsers.

📥 Downloads (30d): 57.7K · 📜 License: Apache 2.0
👤 By: OpenAI · 🎯 Task: token-classification
📐 Size: 1.5B total (50M active)

What it is: A bidirectional token classifier using sparse MoE with 128 experts that detects eight categories of personally identifiable information in a single forward pass with 128K context. Why you'd want it: On-premises PII redaction before sending data to external APIs - small enough for browsers or laptops.

✓ Pros	✗ Cons
Apache 2.0, runs in browser	Only 8 PII categories
50M active params = very fast	Newer, less tested than Presidio
First open PII model from OpenAI	English-focused

#6

zai-org/GLM-5.1

Zhipu AI's 754B flagship targeting autonomous software engineering with best-in-class SWE-Bench Pro.

📥 Downloads (30d): 256K · 📜 License: MIT
👤 By: Z.ai (Zhipu AI) · 🎯 Task: text-generation
📐 Size: 754B

What it is: A bilingual (English + Chinese) MoE model designed for agentic engineering tasks requiring iterative coding over many tool-calling rounds. 58.4% on SWE-Bench Pro, 95.3% on AIME 2026. Why you'd want it: One of the strongest open models for autonomous GitHub issue resolution and long-horizon agent tasks - particularly relevant for Chinese-language software teams.

✓ Pros	✗ Cons
58.4% SWE-Bench Pro (#1 open model)	754B requires serious infrastructure
MIT license, bilingual	Less Western community adoption
Built for multi-round tool calling	Newer, less ecosystem support

#7

nvidia/Nemotron-3-Nano-Omni-30B-A3B

NVIDIA's any-to-any model unifying video, audio, image, and text in one architecture.

📥 Downloads (30d): 9.82K · 📜 License: NVIDIA Open Model Agreement
👤 By: NVIDIA · 🎯 Task: any-to-any
📐 Size: 31B

What it is: A Mamba2-Transformer hybrid MoE released April 28 that processes video (up to 2 min), audio (up to 1 hour), images, and text in a single model. Outputs text, reasoning traces, JSON, tool calls, and word-level timestamps. Why you'd want it: Replaces separate transcription, vision, and language models with one unified model - available in FP8 and NVFP4 quantizations for consumer hardware.

✓ Pros	✗ Cons
True omni-modal (4 input types)	NVIDIA-specific license
FP8 and NVFP4 quantizations	2-min video limit
Enterprise workflow features	Very new, limited community testing

#8

tencent/HY-World-2.0

Converts images and video into navigable, editable 3D scenes as meshes or Gaussian Splattings.

📥 Downloads (30d): 3.13K · 📜 License: Tencent Community
👤 By: Tencent · 🎯 Task: image-to-3D
📐 Size: ~1.2B

What it is: A four-stage pipeline that produces real 3D assets - meshes and 3D Gaussian Splattings - from text, images, or video. Outputs work directly in Blender, Unreal Engine, and NVIDIA Isaac Sim. Why you'd want it: Unlike video world models, the outputs are production-ready 3D assets for game development, VR/AR, and robotics - renderable in real-time on consumer GPUs.

✓ Pros	✗ Cons
Real 3D assets, not just video	Community license restrictions
Works with Blender, Unreal, Isaac Sim	Relatively small downloads
Multi-input (text, image, video, multi-view)	Newer pipeline, less documentation

Product Hunt

AI Launches Today

Plurai

Vibe-train evals and guardrails tailored to your use case

🔥 Upvotes: 510 · 👤 By: Plurai (Tammy Wolfson, Omri Sela)
💰 Pricing: freemium · 🏷 Category: AI Evaluation

Plurai builds production-grade AI evals from plain-language task descriptions - no labeled data needed. Uses small models tuned via "vibe-training" for sub-100ms latency and 8x lower cost than GPT-as-judge. Verdict: A genuinely differentiated approach to LLM evaluation - replacing expensive LLM-as-judge with optimized small models is compelling, though enterprise accuracy claims need independent verification.

Gro v2

Spot signals, trigger outreach - turn posts into pipeline

🔥 Upvotes: 235 · 👤 By: Gro (Zac Zuo)
💰 Pricing: paid (credits) · 🏷 Category: AI Sales

AI sales co-pilot that monitors social signals to identify buying intent, then triggers personalized outreach across email and LinkedIn. Draws on a 650M+ contact database. Verdict: Signal-based outreach is crowded (Clay, Apollo compete), but the social-post-to-pipeline angle is a distinct hook - real value depends on intent signal quality.

KarmaBox

Run your own Claude Code in your pocket

🔥 Upvotes: 206 · 👤 By: Karma AI Factory
💰 Pricing: freemium · 🏷 Category: AI Infrastructure

A local-first multi-agent orchestration system with a free iOS app. Coordinates agents using on-device models (Llama/Qwen) with persistent memory. Verdict: "Claude Code in your pocket" is marketing stretch - it's actually a local agent orchestration layer, which is solid but the tagline oversells it.

CodeHealth MCP Server

Keep AI-generated code healthy and maintainable

🔥 Upvotes: 187 · 👤 By: CodeScene (Adam Tornhill)
💰 Pricing: freemium · 🏷 Category: Developer Tools

Plugs deterministic code-health analysis into AI coding agents via MCP, providing real-time feedback on technical debt without using another AI as judge. Verdict: The most technically substantive launch of the day - CodeScene has years of code-health research behind it, and deterministic quality feedback for AI-generated code addresses a real gap.

Dreambase Data Agent Skills

Analytical skills for data agents running on Supabase

🔥 Upvotes: 134 · 👤 By: Dreambase
💰 Pricing: freemium · 🏷 Category: Data Analysis

Pre-built analytical skill modules for Supabase-backed data agents - querying, aggregating, and interpreting data without custom prompt engineering. Verdict: Niche but practical for the Supabase community; broad appeal is limited unless it expands to other backends.

API Pricing

Snapshot

Provider	Model	Input $/1M	Output $/1M	Context
Anthropic	Claude Opus 4.7	$5.00	$25.00	1M
Anthropic	Claude Sonnet 4.6	$3.00	$15.00	1M
Anthropic	Claude Haiku 4.5	$1.00	$5.00	200K
OpenAI	GPT-4.1	$2.00	$8.00	1M
OpenAI	GPT-4o	$2.50	$10.00	128K
Google	Gemini 3.1 Pro Preview	$2.00	$12.00	200K
Google	Gemini 2.5 Pro	$1.25	$10.00	1M
Google	Gemini 2.5 Flash	$0.30	$2.50	1M
Groq	Llama 4 Scout (17Bx16E)	$0.11	$0.34	128K
Groq	Llama 3.3 70B	$0.59	$0.79	128K
DeepSeek	V4 Flash	$0.14	$0.28	1M

What this means: DeepSeek V4 Flash is now the cheapest frontier-class model by a wide margin - 14 cents per million input tokens with a million-token context window. That's 21x cheaper than Claude Sonnet and 14x cheaper than GPT-4.1. Groq's Llama models are even cheaper per token but lack the million-token context. The price compression at the bottom of this table is extraordinary: two years ago, this performance tier cost $30+ per million tokens.

arXiv Paper of the Day

How Do AI Agents Spend Your Money? Analyzing and Predicting Token Consumption in Agentic Coding Tasks

Longju Bai, Zhemin Huang, Xingyao Wang, Jiao Sun, Rada Mihalcea, Erik Brynjolfsson, Alex Pentland, Jiaxin Pei - arXiv:2604.22750

What it claims: This is the first systematic study of token consumption across eight frontier AI models on agentic coding tasks (SWE-bench Verified). Agents consume roughly 1,000x more tokens than code reasoning or chat, driven primarily by input tokens, and models cannot reliably predict their own costs before running.

Key finding: Token usage for the same task varies by up to 30x across runs, and frontier models' self-cost predictions correlate with actual usage at r = 0.39 or lower - some models consume over 1.5 million more tokens than GPT-5 on identical tasks with no accuracy improvement.

Why practitioners should care: Teams deploying coding agents are likely flying blind on costs. The models themselves underestimate usage, high token counts don't mean better results, and the variance makes budgeting unreliable without empirical measurement. This paper provides a framework for benchmarking and controlling agent spend.

Read on arXiv →

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Snapshot

How Do AI Agents Spend Your Money? Analyzing and Predicting Token Consumption in Agentic Coding Tasks

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