Brady Long

RIP paid AI memory tools. MemPalace just hit 49K stars for one reason: it outperforms everything, runs locally, costs nothing, and never sends your data anywhere. Most AI memory systems work like a junk drawer. You throw everything in and hope semantic search finds the right thing. MemPalace organizes memory into a structured palace where people and projects get wings, topics get rooms, and original text lives in drawers that can be searched individually without crawling the whole corpus. The benchmark numbers are what shocked me. 96.6% retrieval recall on LongMemEval with zero API calls, zero LLM, zero cloud involvement. Add the hybrid pipeline and it hits 98.4% on questions the system had never seen. Every result is fully reproducible from the repo. → Verbatim storage, nothing gets summarized or rewritten ever → 29 MCP tools for palace reads, knowledge graph operations, cross-wing navigation, and agent diaries → Auto-save hooks for Claude Code keep memory updated before context compression hits → Python 3.9 plus ChromaDB is all you need, around 300MB disk and zero API keys 49K stars. MIT License. 100% Opensource. github.com/MemPalace/mempala…

This is absolutely WILD. The developer changed ONE line in a knowledge graph. The tool uncovered 901 new logical consequences that a basic change report could miss. Now imagine doing that across enterprise AI systems. It's called Open Ontologies. The line said: anything that has a parent is a Person. A normal diff called it safe. 0 classes added. 0 removed. Blast radius zero. Risk: low. But every item that already had a parent quietly became a "Person." 901 new facts, found in 0.04 seconds. Here's what this thing can do: → Plan a change before it hits production, like Terraform for knowledge graphs → Show the blast radius and risk score of every edit → Apply, watch for drift, and roll back when something breaks → Load CSV, JSON, Excel, Parquet, PostgreSQL and DuckDB → Check whether a RAG retrieval still supports the answer → Run inside Claude Code, Claude Desktop and Cursor through MCP Here's why this matters: Most AI systems tell you "trust me, the reasoner said so." This one hands you a proof file. A separate checker, built in the Lean 4 theorem prover, verifies every step. An auditor can re-check it months later with no network and no copy of the tool. Here's the wildest part: Fake one conclusion in the proof and the checker refuses it. It names the exact line and the rule that broke. One Rust binary. No Java. No Protégé. github.com/fabio-rovai/open-…

Someone just built something WILD for Claude Code users. A self-learning system that automatically creates, updates, and organizes its own skills. Instead of repeatedly teaching Claude the same workflows, you let it learn from real sessions. This could change how people use coding agents. It's called Autoharness. No manual SKILL .md writing. No pile of duplicate skills. No background daemon eating your machine. It watches your real Claude Code sessions, pulls out the lessons, and turns them into skills Claude loads next time. Here's what this thing can do: → Writes new skills from the work you already did → Merges near-duplicates instead of stacking them → Rewrites old skills when you correct it → Archives skills that stop getting used → Opens every session with an index of what it learned → Type /learn to save a lesson on the spot → Never touches skills you wrote or installed yourself Install it inside Claude Code: /plugin marketplace add tigerless-labs/autoharness /plugin install autoharness@autoharness github.com/tigerless-labs/au…

THIS IS WILD: Perplexity has a secret mode called "Citation Hunter." It finds real sources, checks claims, compares papers, and builds fully cited research briefs with zero hallucinated references. Most people use Perplexity like a faster Google. Analysts are doing something completely different. Here are 11 prompts they're quietly using to research like a machine 👇

Somewhere, a finance team is building a spreadsheet to explain what changed in last month’s spreadsheet. Add screenshots of bank statements and enough people to hold it all together. We call this “closing the books.” Then do it all again next month. Just listened to Parker Gilbert from @numeric_io break down how to finally get out of this loop.

Google’s NotebookLM just exposed how broken textbooks are. Turn any boring PDF into: - mind maps - quizzes - timelines - audio lessons - personalized examples Here are 10 prompts to build your own AI tutor for free ↓

Elite ML researchers aren't building chatbots anymore. They're building stuff like agentic accounting. I just listened to Matt Harpe from @trybasis explain that and it clicked almost immediately.

Ok this is crazy... Someone built a free self-hosted PaaS that lets you deploy static sites, full-stack apps, databases, and 280+ one-click services on your own servers in seconds with a beautiful UI and zero vendor lock-in. It's called Coolify and it's the open-source alternative to Vercel, Heroku, and Netlify that you actually own and control. Here's everything you need to know:

Tons of AI tools help companies scale operations, CRM etc. But payments are the real constraint. That's where everything breaks. I just listened to @jbjerresanders from @uselightinc explain that. Super interesting.

This is terrifying. Jensen Huang's NVIDIA engineers released their internal AI prompting playbook. No paywalls. No waitlists. No gatekeeping. Your agents are hallucinating 35% more than they should and this stops it cold. Here's the exact system they built and the 6 prompts that changed our results overnight: