Watch today's digest as a video summary (generated by NotebookLM)
By the Numbers
Statistically Speaking
25%
of all federally supported basic research at
Trump Fires the Entire National Science Board
Top Story
465
refusals" and "zero capability loss," but independent
Open-Source AI's Biggest Plagiarism Scandal
21
of 42 model layers, achieving 0% hard
The Uncensored Model Underground Is Growing - And Getting Me
One Thing to Tell Your Friends
One Thing to Tell Your Friends
A 23-year-old with no advanced math training used ChatGPT to solve a problem that stumped mathematicians for 60 years - and the proof used a technique no human researcher had ever thought to try.
Summary
TL;DR
Top Stories
Trends
The Uncensored Model Underground Is Growing, Speculative Decoding Is Having Its Moment, and AI Benchmarks Are Broken and Everyone Knows It.
Creative AI
Dev Tools
Research
Business
Apple's New CEO Signals an AI Hardware Pivot, Anthropic's Support System Draws Fire, and GoDaddy Transferred a 27-Year.
Surprising
"I Don't Feel Like Fixing This Function", Someone Used AI to Explain a Dune Passage That Warns Against Using AI, and Claude Snuck In a New Email Sign.
Worth Watching
Vulkan Support for ik_llama.cpp Needs Volunteer Experts, The Next Generation of Unified Memory Chips Is Coming, and GitNexus: Knowledge Graphs for Codebases (30.1K Stars).
GitHub
Leading repos: mattpocock/skills (+2,507), Alishahryar1/free-claude (+1,694), and abhigyanpatwari/GitNexus (+667).
HuggingFace
Leading models: deepseek-ai/DeepSeek-V4 (123K), z-lab/Qwen3.6-35B-A3B (26.8K), and OBLITERATUS/gemma-4-E4B-it (120K).
Product Hunt
API Pricing
What this means: GPT-5.5's price doubling (from $2.50/$15 to $5/$30) puts it at the same input price as Claude Opus 4.7 but 20% more expensive on output.
arXiv
Agent Psychometrics: Task — The method identifies "diagnostic" benchmark tasks that efficiently separate agent capability levels, reducing the number of test cases needed by an order of magnitude while maintaining evaluation accuracy.
FYI
Hot off the Presses
01
A 23-Year-Old Used ChatGPT to Solve a 60-Year-Old Math Problem
What this means for you: AI isn't just automating known tasks - it's finding solutions that trained experts missed, which changes who can contribute to frontier knowledge.
Liam Price, 23, entered a decades-old conjecture about "primitive sets" (collections of whole numbers where no number divides any other) into ChatGPT Pro on a casual Monday afternoon. The AI applied a formula from a related mathematical field that no previous researcher had considered for this particular problem. Collaborating with Kevin Barreto, a second-year math undergraduate at Cambridge, they produced a valid proof.
- The problem: Paul Erdős conjectured that the lowest possible "Erdős sum" for primitive sets approaches exactly one as the numbers grow larger. Mathematicians had been stuck for 60 years.
- Why AI succeeded: As Fields Medal winner Terence Tao explained, "there was a standard sequence of moves that everyone started by doing." The Large Language Model (a type of AI that processes text) bypassed this conventional path entirely.
- The catch: The raw output "was actually quite poor," according to mathematician Jared Lichtman - it contained valuable insights buried in messy reasoning that required expert interpretation.
- Broader impact: Tao says the discovery reveals "a new way to think about large numbers and their anatomy" with applications beyond this single problem.
02
Claude 4.7 Can Identify You From 125 Words of Your Writing
What this means for you: If you write publicly under your real name, AI may already be able to connect your anonymous accounts to your identity based on writing style alone.
Journalist Kelsey Piper discovered that Claude Opus 4.7 could identify her as the author of unpublished text samples from as few as 125 words - across political commentary, educational content, movie reviews, and even a 15-year-old college application essay.
- It's not content matching. The model picks up on "imperceptible tics in prose" - stylistic fingerprints invisible to human readers. Piper tested via incognito mode, Application Programming Interface (API), and had others replicate on different computers to rule out account tracking.
- Competing models performed worse. ChatGPT and Gemini guessed incorrectly or couldn't identify the author, making this a Claude-specific capability.
- The explanations were "absolute nonsense." The model identified authors correctly but couldn't accurately explain how, suggesting pattern recognition beyond human-legible style analysis.
- Who's at risk: Academics, researchers, and whistleblowers with substantial public writing face deanonymization "within years" as the technology improves.
03
Trump Fires the Entire National Science Board
What this means for you: The body that oversees $9 billion in US science funding - including grants that train the next generation of AI researchers - no longer has any members.
President Trump dismissed all 24 members of the National Science Board (NSB), the statutory oversight body for the National Science Foundation (NSF), via email on April 24. The presidential personnel office wrote simply that their positions were "terminated, effective immediately."
- The NSB approves all major NSF expenditures and sets policy for the agency that funds roughly 25% of all federally supported basic research at US colleges and universities.
- NSF faces a proposed 55% budget cut in the upcoming fiscal year. Dismissed NSB chair Victor McCrary had publicly criticized the cuts.
- No replacements announced. The White House has not explained the firings or indicated when - or whether - new members will be appointed.
- Bipartisan criticism: Rep. Zoe Lofgren called it "the latest stupid move made by a president who continues to harm science and American innovation."
04
Open-Source AI's Biggest Plagiarism Scandal
What this means for you: The most popular "uncensored model" toolkit on HuggingFace was built by copying someone else's code, renaming the variables, and swapping the license - and forensic analysis caught every detail.
A community investigation revealed that HauhauCS's "reaper-abliteration" tool - used to remove safety guardrails from AI models - is plagiarized from an open-source project called Heretic, created by Philipp Emanuel Weidmann.
"Median cosine similarity of 0.966" between edit vectors from HauhauCS's models and original Heretic outputs - near-identical transformations, not independent work.
This story hit 540 upvotes on r/LocalLLaMA and triggered a broader community reckoning about attribution and licensing in the open-source AI ecosystem.
- 60% of Heretic's code was found intact with variable renames. All seven original module filenames, 31 method names, 7 class names, and 30 refusal marker strings were copied - including a truncated string
"i can'"(missing the 't') that proves line-by-line derivation. - The license was swapped from AGPL-3.0 (which requires sharing source code and preserving attribution) to PolyForm Noncommercial - without any credit to the original author.
- Performance claims were fabricated. HauhauCS claimed "0/465 refusals" and "zero capability loss," but independent testing found soft refusals on smaller models and TruthfulQA scores declined 2-8 points.
- Heretic's author confirmed the analysis after reviewing recovered source code, identifying his own SPDX headers, geometric median approach, and specific field names.
05
An AI Coding Agent Deleted a Production Database
What this means for you: AI coding tools with access to production credentials can cause catastrophic damage in seconds - and the fix is infrastructure guardrails, not better prompts.
A Hacker News post (355 points, 491 comments) documented how a developer using Cursor with Claude Opus 4.6 accidentally had an AI agent delete their production database. The agent had access to credentials that weren't properly separated from the development environment.
- Root cause: commingled credentials. Production database credentials were accessible from the same environment where the AI agent was running code experiments.
- No safeguards existed. No confirmation prompts, no read-only defaults, no environment separation prevented the deletion.
- Community consensus: This is an infrastructure failure, not an AI failure. The same mistake could happen with a junior developer who accidentally runs a destructive command.
- The lesson: As AI agents gain more autonomy, the blast radius of a single misconfigured permission grows dramatically. Least-privilege access and environment isolation aren't optional.
Trends & Themes
Trends & Themes
The Uncensored Model Underground Is Growing - And Getting Messy
Why this matters to you: The tools that remove safety filters from AI models are getting more popular and more controversial, raising questions about who controls what AI can say.
The tension is real: users want unrestricted models, but the ecosystem lacks standards for attribution, quality verification, or honest capability claims.
- HauhauCS's uncensored models remain among HuggingFace's most downloaded despite the plagiarism scandal, with the Qwen3.6-35B-A3B variant at 461,000 downloads
- OBLITERATUS released a "guardrail-stripped" Gemma 4 using automated techniques that modified 21 of 42 model layers, achieving 0% hard refusal rate - created almost entirely by an AI agent with fewer than 10 human prompts
- A competing Heretic-based uncensored model (Qwen3.6-35B-A3B-uncensored-heretic) hit 353 upvotes on r/LocalLLaMA, praised for lower capability degradation (KLD of 0.0015)
Speculative Decoding Is Having Its Moment
Why this matters to you: A technique that makes AI respond 2-3 times faster without changing the model's answers is getting practical enough for everyday use.
Speculative decoding (where a small, fast model drafts multiple tokens that a larger model verifies in parallel) is moving from research curiosity to production technique - especially for local inference where every token per second matters.
- DFlash achieves 2.9x speedup on math tasks using a 0.5B "drafter" model alongside Qwen3.6-35B, accepting 7.35 tokens per draft block
- Gemma-4-31B + Gemma-4-E2B enables 120-200 tokens/second for specific tasks through speculative decoding, reported by r/LocalLLaMA users
- A comprehensive implementation guide covering EAGLE-3, Medusa-1, PARD, and N-gram approaches from scratch was shared with code on GitHub
AI Benchmarks Are Broken and Everyone Knows It
Why this matters to you: The scores companies use to claim their AI is "best" are increasingly meaningless - models trained on the test answers score well without actually being better.
"80% on Verified, 23% on Pro" - the gap between memorized answers and actual coding ability.
- OpenAI abandoned SWE-bench Verified after finding 59.4% of audited problems had flawed test cases that reject correct solutions
- All tested frontier models could reproduce original solutions or quote verbatim problem details they should never have seen - clear evidence of data contamination
- The reality gap is stark: models scoring 80% on SWE-bench Verified dropped to roughly 23% on the contamination-resistant SWE-bench Pro
- Community reaction on r/LocalLLaMA (232 upvotes): "Confirmed: SWE Bench is now a benchmaxxed benchmark"
The Agent Tooling Ecosystem Is Exploding
Why this matters to you: Building and managing AI coding agents is becoming a full engineering discipline, with purpose-built tools for memory, governance, and workflow management.
- mattpocock/skills hit 23,400 stars on GitHub with curated agent workflows for planning, TDD, bug triage, and architecture review - all installable via
npx skills@latest add - Beads (21,600 stars) provides graph-based persistent memory for agents using a version-controlled SQL database, with semantic "memory decay" to manage context windows
- Cloudflare shipped enterprise MCP governance including "Shadow MCP" detection (unauthorized AI tool connections), Code Mode reducing token usage by up to 99.9%, and centralized policy enforcement
- awesome-codex-skills (1,900 stars) curates 60+ modular instruction bundles for OpenAI's Codex across development, productivity, and communication tasks
Local Inference Speed Is Getting Genuinely Fast
Why this matters to you: Running AI on your own computer - privately, without internet, and for free - is reaching speeds that were cloud-only six months ago.
- Qwen3.6-27B at 100 tokens/second with 256K context on a single RTX 5090 using INT4 quantization via vLLM (209 upvotes on r/LocalLLaMA)
- Structured Chain-of-Thought cuts thinking tokens by 22x with no accuracy loss by constraining the model's reasoning to a compact grammar-defined format
- vLLM's DeepSeek V4 implementation achieves 8.7x KV cache reduction (9.62 GiB per sequence at 1M context vs. 83.9 GiB estimated for comparable models)
- Windows vs. Linux benchmarks show a significant gap on RTX 5080 + i9-14900KF, with Lubuntu 26.04 outperforming Windows 11 for llama.cpp inference
The Anthropic-Government Standoff Enters a Negotiation Phase
Why this matters to you: The dispute between a leading AI company and the US military over weapons use is moving from courtrooms to deal-making - the outcome will shape what AI companies can refuse to do.
Previously: April 24 - Anthropic lost its appeals court bid to block Pentagon blacklisting.
- Trump said Anthropic is "shaping up" after White House meetings, calling a deal "possible" for Department of Defense use
- Anthropic reportedly dropped its demand for a "kill switch" in classified settings, according to Axios
- The core tension remains: The Pentagon wants unfettered access to Claude for all lawful purposes; Anthropic wants assurances against use in fully autonomous weapons or domestic mass surveillance
- A TechPolicy.Press timeline documents the full arc from Anthropic's $200M contract through the February ultimatum, March lawsuits, and April negotiations
Creative AI & Media
Creative AI & Media
AI-Generated Videos Are Drowning Out Real Content
What this means for you: The flood of AI-generated video content is making social media feeds harder to trust and less enjoyable to browse.
- "These generated videos have ruined the fun of social media and YouTube" resonated on r/artificial as users report increasing difficulty distinguishing real from generated content
- A side-by-side test of 6 AI video tools for ads and content found wide variation in quality, with no single tool dominating across all use cases
- The "era of all-you-can-eat AI" may be ending as subscription fatigue sets in and providers consider usage-based pricing
Ruben's AI Tool Stack: Why One AI Isn't Enough
- Claude for writing (contracts, briefs, memos) with Cowork's folder integration for context
- ChatGPT 5.5 for images and search with dramatically improved visual generation
- Gemini for non-English content; Gamma for presentations
- The pragmatic verdict: specialization beats generalization in the current market
Developer Tools
Developer Tools & Infrastructure
Research & Models
Research & Models
Business & Industry
Business & Industry
Surprising
Surprising & Under-the-Radar
"I Don't Feel Like Fixing This Function" - And That's Now a Valid Strategy
A r/LocalLLaMA post (159 upvotes) captured a moment of realization: "I can't believe I can say 'ugh I don't feel like fixing this function, it's too complex' and I can literally just tell my computer to fix it for me. I didn't understand what they meant by 'people will start paying for intelligence' but now I do."
Someone Used AI to Explain a Dune Passage That Warns Against Using AI
A r/artificial post noted the irony of using AI to explain Frank Herbert's famous passage about the dangers of letting machines do your thinking. "That's the whole debate" - 6 upvotes, but perfectly encapsulates the cultural moment.
Claude Snuck In a New Email Sign-Off
Users on r/ClaudeAI noticed Claude has started using unexpected email sign-offs in generated messages - a small but entertaining personality quirk.
Opus 4.7 Serializes File Reads, Costing More Tokens
A technical observation on r/ClaudeAI: Claude Code with Opus 4.7 appears to read files sequentially rather than in parallel, causing higher token usage compared to Opus 4.6 for the same tasks.
The Linux Kernel's Bug-Hunting AI Bot Runs Locally
The Clanker T1000, a bot that finds bugs in the Linux kernel, runs on a Framework Desktop with AMD Ryzen AI Max - a local Large Language Model doing real systems-level work without cloud dependencies.
AI Writing Analysis Creates Contradictory Incentives
The same tool that can verify authorship (Claude identifying writers from 125 words) could also be used to strip identifying patterns - creating an arms race between attribution and anonymity.
Worth Watching
Signals to Track
01
Vulkan Support for ik_llama.cpp Needs Volunteer Experts
A call for GPU programming experts could unlock fast AI inference on non-NVIDIA hardware.
The ik_llama.cpp fork (an optimized llama.cpp variant) is seeking Vulkan experts to add GPU acceleration support. A Mesa merge request already shows 37-130% performance gains for llama.cpp on Intel Xe2 GPUs via Vulkan on Linux. If Vulkan support matures, it could break NVIDIA's near-monopoly on local AI inference hardware.
02
The Next Generation of Unified Memory Chips Is Coming
Three chip architectures launching this year could make running large AI models on a desktop practical.
A r/LocalLLaMA comparison of upcoming x86 unified memory systems - AMD Gorgon Halo, AMD Medusa Halo, and Intel Nova Lake AX - shows significantly more memory bandwidth than current Strix Halo chips. Unified memory (where CPU and GPU share one pool) is the key enabler for running large models locally.
03
GitNexus: Knowledge Graphs for Codebases (30.1K Stars)
AI agents that understand code structure before they start editing could prevent entire categories of bugs.
GitNexus builds knowledge graphs from codebases entirely in the browser, exposing 16 MCP tools for AI agents to query dependencies, execution flows, and functional clusters before making changes. At 30,100 stars and rising, it signals demand for "code understanding" as a distinct layer in the AI development stack.
04
Grammar-Constrained Reasoning Could Slash AI Costs
Forcing AI to think in structured formats instead of rambling could cut inference costs by 95% for some tasks.
Structured CoT demonstrated that grammar constraints on reasoning improve both speed and accuracy. If adopted broadly, this technique could make "thinking" models (which currently cost 5-10x more due to lengthy internal reasoning) affordable for everyday use.
05
The "Slop Detection" Category Is Emerging
As more code is written by AI, tools to catch AI-specific quality problems are becoming a new software category.
Lipstyk (static analysis for AI-generated code patterns) joins a growing set of tools addressing the quality gap in agent-written code. With developers reporting that "agents write most of my code," the need for AI-aware linting and quality gates is becoming urgent.
GitHub Trending
Top Repos Today
Rank yesterday: #2 - Holding steady ➡
⭐ Stars today: +2,507 · 📦 Total: 23,363
📜 License: MIT · 👤 By: individual developer
🎯 Time to value: 2 minutes
📜 License: MIT · 👤 By: individual developer
🎯 Time to value: 2 minutes
What it is: A curated collection of reusable AI agent workflows designed for professional software engineering. Skills cover planning (PRD generation, vertical-slice issue breakdown), development (TDD cycles, bug triage, architecture review), tooling (pre-commit hooks, Git safety), and writing (article editing, Obsidian integration). All installable via
npx skills@latest add. Why you'd want it: Instead of writing custom prompts for every engineering task, you get battle-tested workflows from a well-known developer educator. Each skill encodes a repeatable process, not just a one-shot prompt.| ✓ Pros | ✗ Cons |
|---|---|
| MIT licensed, instantly installable | Shell-based, may not fit all agent setups |
| Covers real engineering workflows, not toy demos | Limited to Claude Code ecosystem currently |
| Active development with 34 commits | Some skills are opinionated about workflow |
GitHub - mattpocock/skills: Agent Skills for real engineers. Straight from my .claude directory.
Agent Skills for real engineers. Straight from my .claude directory. - mattpocock/skills
GitHub
Rank yesterday: #1 - Falling ↓
⭐ Stars today: +1,694 · 📦 Total: 13,465
📜 License: MIT · 👤 By: individual developer
🎯 Time to value: 5 minutes
📜 License: MIT · 👤 By: individual developer
🎯 Time to value: 5 minutes
What it is: A proxy that lets you use Claude Code's terminal interface, VS Code extension, or Discord bot without an Anthropic API subscription. Routes requests through alternative providers while maintaining the Claude Code experience. Why you'd want it: Free access to Claude Code's agentic coding capabilities for developers who can't justify the API costs. Already covered in previous editions - still trending but growth is slowing. > Previously: April 23 - first appeared on GitHub trending.
| ✓ Pros | ✗ Cons |
|---|---|
| Genuinely free Claude Code experience | Relies on third-party API providers |
| Works across terminal, VS Code, Discord | May violate Anthropic's terms of service |
| Active community and frequent updates | Quality depends on underlying provider |
GitHub - Alishahryar1/free-claude-code: Use claude-code for free in the terminal, VSCode extension or via discord like openclaw
Use claude-code for free in the terminal, VSCode extension or via discord like openclaw - Alishahryar1/free-claude-code
GitHubRank yesterday: not ranked - New entry 🆕
⭐ Stars today: +667 · 📦 Total: 30,118
📜 License: PolyForm Noncommercial · 👤 By: startup (Akon Labs)
🎯 Time to value: 3 minutes
📜 License: PolyForm Noncommercial · 👤 By: startup (Akon Labs)
🎯 Time to value: 3 minutes
What it is: A code intelligence platform that constructs knowledge graphs from codebases. Runs entirely in the browser using Tree-sitter for AST parsing, with hybrid BM25 + semantic search, dependency analysis, process detection, and git-diff impact analysis. Supports 10+ languages including TypeScript, Python, Java, Go, and Rust. Why you'd want it: AI agents make fewer mistakes when they understand code structure before editing. GitNexus exposes 16 MCP tools that let Claude Code, Cursor, or Codex query your codebase's dependency chains and execution flows.
| ✓ Pros | ✗ Cons |
|---|---|
| Runs client-side, no server needed | PolyForm Noncommercial license limits commercial use |
| 16 MCP tools for agent integration | Large codebases may be slow to index |
| Multi-language with hybrid search | Enterprise features require paid plan |
GitHub - abhigyanpatwari/GitNexus: GitNexus: The Zero-Server Code Intelligence Engine - GitNexus is a client-side knowledge graph creator that runs entirely in your browser. Drop in a GitHub repo or ZIP file, and get an interactive knowledge graph wit a built in Graph RAG Agent. Perfect for code exploration
GitNexus: The Zero-Server Code Intelligence Engine - GitNexus is a client-side knowledge graph creator that runs entirely in your browser. Drop in a GitHub repo or ZIP file, and get an intera…
GitHub
Rank yesterday: not ranked - New entry 🆕
⭐ Stars today: +133 · 📦 Total: 21,646
📜 License: MIT · 👤 By: organization
🎯 Time to value: 5 minutes
📜 License: MIT · 👤 By: organization
🎯 Time to value: 5 minutes
What it is: A distributed graph issue tracker designed as persistent, structured memory for AI coding agents. Built on Dolt (a version-controlled SQL database), it replaces markdown plan files with a dependency-aware graph system featuring hash-based IDs to prevent merge conflicts and semantic "memory decay" for context window management. Why you'd want it: If you run multi-step AI coding tasks that lose context between sessions, Beads gives agents a structured way to track dependencies, manage sub-tasks, and remember what they've already done.
| ✓ Pros | ✗ Cons |
|---|---|
| MIT licensed, multi-platform | Requires Dolt database (extra dependency) |
| Graph relationships prevent lost context | Learning curve for graph-based thinking |
| Multi-agent and multi-branch support | Relatively new, API may change |
GitHub - gastownhall/beads: Beads - A memory upgrade for your coding agent
Beads - A memory upgrade for your coding agent. Contribute to gastownhall/beads development by creating an account on GitHub.
GitHub
Rank yesterday: #5 - Holding steady ➡
⭐ Stars today: +200 · 📦 Total: 14,344
📜 License: MIT · 👤 By: startup
🎯 Time to value: 10 minutes
📜 License: MIT · 👤 By: startup
🎯 Time to value: 10 minutes
What it is: Open-source infrastructure for building AI agents that can control full desktop environments across macOS, Linux, Windows, and Android. Includes sandboxed VMs, SDKs, benchmarks (OSWorld, ScreenSpot, Windows Arena), and a background automation driver for macOS that doesn't steal cursor focus. Why you'd want it: If you're building or researching "computer-use" AI agents - the kind that can click buttons, fill forms, and navigate GUIs autonomously - this is the testing and deployment infrastructure.
| ✓ Pros | ✗ Cons |
|---|---|
| Multi-OS support including macOS VMs | Complex setup for full multi-OS testing |
| Background automation preserves user control | macOS VM requires Apple Silicon |
| Active community (33K+ Discord members) | Still evolving rapidly |
GitHub - trycua/cua: Open-source infrastructure for Computer-Use Agents. Sandboxes, SDKs, and benchmarks to train and evaluate AI agents that can control full desktops (macOS, Linux, Windows).
Open-source infrastructure for Computer-Use Agents. Sandboxes, SDKs, and benchmarks to train and evaluate AI agents that can control full desktops (macOS, Linux, Windows). - trycua/cua
GitHub
Rank yesterday: not ranked - New entry 🆕
⭐ Stars today: +518 · 📦 Total: 1,942
📜 License: Open source · 👤 By: startup (Composio)
🎯 Time to value: 5 minutes
📜 License: Open source · 👤 By: startup (Composio)
🎯 Time to value: 5 minutes
What it is: A curated collection of 60+ modular instruction bundles ("skills") for OpenAI's Codex. Skills span development (codebase migration, PR review, CI/CD fixes), productivity (issue triage, meeting analysis, Notion integration), communication (email drafting, changelog creation), and data analysis (spreadsheet formulas, competitor analysis). Why you'd want it: Pre-built workflows for Codex that connect to 1,000+ apps via Composio's integration platform. Think of it as mattpocock/skills but for the OpenAI ecosystem.
| ✓ Pros | ✗ Cons |
|---|---|
| 60+ ready-made workflows | Tied to Composio platform for integrations |
| Covers dev, productivity, and communication | Newer, less battle-tested than alternatives |
| Automated installer script | Some skills require paid Composio tier |
GitHub - ComposioHQ/awesome-codex-skills: A curated list of practical Codex skills for automating workflows across the Codex CLI and API.
A curated list of practical Codex skills for automating workflows across the Codex CLI and API. - ComposioHQ/awesome-codex-skills
GitHub
HuggingFace Trending
Top Models Today
The largest open-weight model ever released, now dominating downloads as the community explores its capabilities.
📥 Downloads (30d): 123K · 📜 License: DeepSeek
👤 By: DeepSeek AI · 🎯 Task: Text Generation
📐 Size: 862B (49B active)
👤 By: DeepSeek AI · 🎯 Task: Text Generation
📐 Size: 862B (49B active)
What it is: DeepSeek's flagship Mixture-of-Experts model with 1.6 trillion total parameters and 49 billion active per inference. Supports 1M token context with a novel multi-head latent attention mechanism that dramatically reduces KV cache requirements. Why you'd want it: The most capable open-weight model available, competitive with closed frontier models on most benchmarks. Requires significant hardware but offers full transparency into weights and architecture. > Previously: April 24 - DeepSeek V4 launched with community excitement.
| ✓ Pros | ✗ Cons |
|---|---|
| Most capable open model available | Requires multi-GPU setup for full precision |
| 1M token context with efficient KV cache | DeepSeek license has commercial restrictions |
| Active community tooling (vLLM, GGUF) | Chinese origin raises some enterprise concerns |
deepseek-ai/DeepSeek-V4-Pro · Hugging Face
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
A tiny 0.5B drafter model that makes Qwen3.6 respond 2.9x faster through speculative decoding.
📥 Downloads (30d): 26.8K · 📜 License: MIT
👤 By: Z Lab (research) · 🎯 Task: Text Generation
📐 Size: 0.5B
👤 By: Z Lab (research) · 🎯 Task: Text Generation
📐 Size: 0.5B
What it is: A block diffusion drafter model designed specifically for speculative decoding with Qwen3.6-35B-A3B. Instead of generating tokens one at a time, it drafts multiple tokens in parallel that the larger model verifies. The result is the same output quality at dramatically higher throughput. Why you'd want it: If you run Qwen3.6-35B locally, adding this 0.5B model nearly triples your inference speed on math and code tasks with zero quality loss.
| ✓ Pros | ✗ Cons |
|---|---|
| 2.9x speedup, MIT licensed | Only works with Qwen3.6-35B-A3B target |
| Tiny model (0.5B), minimal overhead | Requires vLLM with specific PR branch |
| Published paper with reproducible results | Speedup varies by task type |
z-lab/Qwen3.6-35B-A3B-DFlash · Hugging Face
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Google's Gemma 4 with all safety guardrails surgically removed - created almost entirely by an AI agent.
📥 Downloads (30d): 120K · 📜 License: Apache 2.0
👤 By: community · 🎯 Task: Text Generation
📐 Size: 4B
👤 By: community · 🎯 Task: Text Generation
📐 Size: 4B
What it is: A modified version of Google's Gemma 4 E4B with safety refusal mechanisms removed using an "aggressive" technique (whitened SVD + attention head surgery). Notable for being created by a Hermes Agent AI with fewer than 10 human prompts. The v3 fix addressed critical NaN activation issues. Why you'd want it: A small, fast, unrestricted model for research and creative exploration where safety filters get in the way.
| ✓ Pros | ✗ Cons |
|---|---|
| 0% hard refusal rate, Apache 2.0 license | ~20% degenerate outputs |
| Small enough for mobile (4.9 GB Q4) | Only ~51% of responses are coherent + on-topic |
| Created almost autonomously by AI | Explicitly unsuitable for user-facing products |
OBLITERATUS/gemma-4-E4B-it-OBLITERATED · Hugging Face
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
The most downloaded uncensored model - now at the center of a plagiarism scandal.
📥 Downloads (30d): 461K · 📜 License: Disputed
👤 By: individual · 🎯 Task: Image-Text-to-Text
📐 Size: 35B
👤 By: individual · 🎯 Task: Image-Text-to-Text
📐 Size: 35B
What it is: An uncensored version of Qwen3.6-35B-A3B that removes safety guardrails using the "reaper-abliteration" technique. The model remains popular by downloads but the underlying tool has been exposed as plagiarized from the Heretic project (see Top Stories). Why you'd want it: Despite the controversy, the model itself still functions. But the plagiarism scandal and fabricated benchmark claims mean you should verify quality independently.
| ✓ Pros | ✗ Cons |
|---|---|
| Very popular, large community | Underlying tool is plagiarized code |
| Based on strong Qwen3.6 base | Performance claims were fabricated |
| Available in multiple quantizations | License status is disputed |
HauhauCS/Qwen3.5-35B-A3B-Uncensored-HauhauCS-Aggressive · Hugging Face
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
A single model that both understands and creates images - a different approach to multimodal AI.
📥 Downloads (30d): 346 · 📜 License: Apache 2.0
👤 By: InclusionAI · 🎯 Task: Any-to-Any
📐 Size: 16B
👤 By: InclusionAI · 🎯 Task: Any-to-Any
📐 Size: 16B
What it is: A unified model for both understanding and generating images, using a diffusion-based approach rather than the autoregressive method most language models use. Handles text-to-image, image-to-text, and image editing in a single architecture. Why you'd want it: Instead of needing separate models for image understanding and generation, one 16B model handles both - useful for creative workflows and multimodal applications. > Previously: April 25 - LLaDA2.0-Uni covered in Creative AI section.
| ✓ Pros | ✗ Cons |
|---|---|
| Single model for understanding + generation | Very early (346 downloads) |
| Apache 2.0 license | Quality likely below specialized models |
| Novel diffusion-based approach | Limited community tooling so far |
inclusionAI/LLaDA2.0-Uni · Hugging Face
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Product Hunt
AI Launches Today
"OpenAI's smartest and most intuitive to use model yet"
🔥 Upvotes: 338 · 👤 By: OpenAI
💰 Pricing: API usage-based ($5/$30 per M tokens) · 🏷 Category: Foundation Models
💰 Pricing: API usage-based ($5/$30 per M tokens) · 🏷 Category: Foundation Models
OpenAI's latest flagship model, launched on the API April 23 and now on Product Hunt. Doubles the price of GPT-5.4 but adds improved reasoning, coding, and multimodal capabilities. The Batch API offers the old pricing ($2.50/$15) for non-real-time workloads. Verdict: The strongest general-purpose API model from OpenAI, but the price doubling is pushing cost-sensitive developers toward open-source alternatives.
View on Product Hunt →
"New connectors in Claude for everyday life"
🔥 Upvotes: 292 · 👤 By: Anthropic
💰 Pricing: included in all Claude plans · 🏷 Category: AI Workflow Automation
💰 Pricing: included in all Claude plans · 🏷 Category: AI Workflow Automation
Anthropic expanded Claude's connector ecosystem from work apps to personal use - now including Spotify, Uber, Instacart, TurboTax, TripAdvisor, Booking.com, Resy, StubHub, and more. The directory has grown to 200+ integrations since launching in July. Verdict: The most meaningful step toward Claude as a personal assistant rather than just a chat interface. Data from connected apps is not used for training.
New connectors in Claude for everyday life | Claude Connectors | Product Hunt
Claude now connects with 200+ apps like Spotify, Instacart, Uber, and TripAdvisor, letting you book, order, and plan without leaving a single conversation. Designed for both work and daily life, it brings travel, food, and entertainment together intelligently suggesting and executing tasks in real time.
Product Hunt
"Compare LLMs on your data, measure, and pick the best"
🔥 Upvotes: 168 · 👤 By: Trismik
💰 Pricing: freemium · 🏷 Category: AI Metrics and Evaluation
💰 Pricing: freemium · 🏷 Category: AI Metrics and Evaluation
Evaluate 50+ models against your own custom datasets to find the best fit for your specific use case. Addresses the growing problem of benchmark gaming by letting you test what matters to your application. Verdict: Timely given the SWE-bench contamination revelations. Testing on your own data is the only reliable way to compare models now.
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"Strava for your coding assistants"
🔥 Upvotes: 119 · 👤 By: Edgee
💰 Pricing: not specified · 🏷 Category: AI Infrastructure Tools
💰 Pricing: not specified · 🏷 Category: AI Infrastructure Tools
Prompt compression technology that reduces token expenses by approximately 50%. Positions itself as observability for AI coding workflows - tracking which prompts cost the most and where tokens are wasted. Verdict: Interesting concept as AI coding costs become a real line item for engineering teams. The "Strava for coding assistants" framing is clever but untested.
View on Product Hunt →
API Pricing
Snapshot
| Provider | Model | Input $/1M | Output $/1M | Context |
|---|---|---|---|---|
| Anthropic | Claude Opus 4.7 | $5.00 | $25.00 | 200K |
| Anthropic | Claude Sonnet 4.6 | $3.00 | $15.00 | 200K |
| Anthropic | Claude Haiku 4.5 | $1.00 | $5.00 | 200K |
| OpenAI | GPT-5.5 | $5.00 | $30.00 | 128K |
| OpenAI | GPT-5.5 Pro | $30.00 | $180.00 | 128K |
| Gemini 3.1 Pro | $2.00 | $12.00 | 1M | |
| Gemini 2.5 Flash | $0.30 | $2.50 | 1M | |
| Groq | Llama 3.3 70B | $0.59 | $0.79 | 128K |
| Groq | Llama 3.1 8B | $0.05 | $0.08 | 128K |
What this means: GPT-5.5's price doubling (from $2.50/$15 to $5/$30) puts it at the same input price as Claude Opus 4.7 but 20% more expensive on output. Google's Gemini 3.1 Pro offers 5x the context window at 40% of the input cost and less than half the output cost. For cost-sensitive workloads, Groq's Llama 3.3 70B at $0.59/$0.79 is 8-38x cheaper than frontier models. All providers offer 50% batch discounts; Anthropic and Google offer up to 90% savings via prompt caching.
arXiv Paper of the Day
Agent Psychometrics: Task-Level Performance Prediction in Agentic Coding Benchmarks
Chris Ge, Daria Kryvosheieva, Daniel Fried, Uzay Girit, Kaivalya Hariharan - arXiv:2604.00594
What it claims: By applying psychometric techniques (the same statistical methods used to design standardized tests for humans) to AI coding benchmarks, you can predict which specific tasks an agent will pass or fail - before running them.
Key finding: The method identifies "diagnostic" benchmark tasks that efficiently separate agent capability levels, reducing the number of test cases needed by an order of magnitude while maintaining evaluation accuracy.
Why practitioners should care: As AI coding benchmarks become contaminated (see SWE-bench story), this approach offers a way to build more robust evaluations. Instead of running thousands of tasks, you run a carefully selected subset that maximally discriminates between capability levels - harder to game and faster to execute.
Key finding: The method identifies "diagnostic" benchmark tasks that efficiently separate agent capability levels, reducing the number of test cases needed by an order of magnitude while maintaining evaluation accuracy.
Why practitioners should care: As AI coding benchmarks become contaminated (see SWE-bench story), this approach offers a way to build more robust evaluations. Instead of running thousands of tasks, you run a carefully selected subset that maximally discriminates between capability levels - harder to game and faster to execute.
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