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A 26-YEAR-OLD AI ENGINEER WHOSE BIRMINGHAM THESIS OUTPERFORMED GOOGLE SCHOLAR BY 50% JUST SHIPPED GRAPHIFY: ONE COMMAND TURNS ANY FOLDER INTO A CLAUDE CODE SECOND BRAIN
Safi Shamsi built Graphify 48 hours after Karpathy posted his LLM wiki idea. It turns any folder, codebase, docs, PDFs, into a knowledge graph Claude reads instead of grepping. Up to 43x fewer tokens per query. The trick almost nobody is using yet: one flag exports the entire graph as a fully-linked Obsidian vault.
Repo: /safishamsi/graphify
Setup, end to end:
1. Install: uv tool install graphifyy (or pipx install graphifyy). Verify with graphify --version.
2. Install the skill in Claude Code: graphify claude install. This wires Graphify into Claude Code so you can call it as a skill.
3. Open the folder you want mapped in Claude Code. In the terminal: graphify . It extracts every concept and builds the graph in graphify-out/.
4. Export to Obsidian: graphify . --obsidian. Writes one note per concept, every relationship as a wikilink, every node linked back to its source.
5. Open the new vault in Obsidian (Manage vaults → Open folder as a vault), or drag it as a subfolder into your existing one.
That's it. Your Claude Code instance now has a navigable map of the codebase that loads instantly instead of re-reading files every session.
Full step-by-step build of the Claude + Obsidian second brain in the article below.
Bookmark this
"The role shifts from maker to architect. From author to managing editor. From preserving code to designing for its replacement ... Resisting the shift doesn't stop it. It just makes systems more fragile." https://t.co/RUo7W2hylu < good post on death and rebirth of programming
An asian guy has discovered a method to learn anything ten times faster using AI!
It just involves the Claude + Obsidian.
Most people learn the slow way: read, forget, re-read, forget again.
His flip: use Claude to turn anything you're learning into small, connected notes. Use Obsidian to link them so nothing you learn ever sits alone.
The slow way: highlight a book, move on, forget it in a week.
The fast way: Claude breaks it into atomic notes, and Obsidian links them into a growing web of knowledge.
Six months in, one new idea instantly connects to twenty things you already know.
I broke down every Claude resource you should try to master claude in 7 days with practical guide that most people have never found.
Article below ↓
A year to two ago, I offered a similar test: train an LLM on medical literature prior to 1890, and see if it would have 'discovered' the role of microorganisms in disease.
What once began as a noble experiment in forming a digital currency unencumbered by financial institutions, cryptocurrencies have proven to be an abject failure by every measure.
There are on the order of only 30 million bitcoin wallets whose value is greater than $100 US; this represents at best about 0.36% of the world’s population, hardly a sufficient number to provide a global medium of exchange. While there certainly exist some fringe cases of utility, most notably by individuals needing to transport value outside dictatorial countries with broken and oppressive economies, cryptocurrencies are largely an object of speculation, driven by market manipulation and the economy of the greater fool and favored by criminal organizations and countries such as Russia and North Korea seeking to avoid sanctions. The ancillary industries spawned by cryptocurrencies – NFT and DeFi and so-called Web3 - never gained traction, and indeed proved to enrich a small few at the expense of the many.
At the start of this technology, I was curious and hopeful, but it is clear that human greed has irredeemably ruined it for all.
🤯 GLM-5.2 is here — built for long-horizon coding and agentic tasks, now with a solid 1M-token context.
The strongest open-source coding model yet!
Available now on Ollama's cloud, hosted in the US on the latest @NVIDIAAI Blackwell datacenter GPUs. Privacy policy and zero data retention apply, as always.
Try it 👇
Claude Code:
ollama launch claude --model glm-5.2:cloud
Codex App:
ollama launch codex-app --model glm-5.2:cloud
Hermes Agent:
ollama launch hermes --model glm-5.2:cloud
Chat:
ollama run glm-5.2:cloud
More integrations and information in the model page 🧵
If you’re a software engineer worried about AI eating your job, become the person who can deploy, customize, evaluate, and operate *****open-source models***** inside companies. Organizations are finally optimizing for AI cost, privacy, and control and many will want this capability in-house.
The University of Michigan put their entire robotics degree on GitHub.
Not one course. The whole curriculum.
ROB 101 — Computational Linear Algebra for Robotics
ROB 311 — How to Build Robots and Make Them Move
ROB 501 — Mathematics for Robotics
ROB 530 — Mobile Robotics
Every lecture video on YouTube. Every textbook on GitHub. Every problem set, every exam, every line of code.
Professor Jessy Grizzle said it best when they launched it:
"Linear algebra has become the language of computer vision, machine learning, robotics, and autonomy."
So instead of making students wait four semesters of calculus before touching a robot... they built a curriculum that starts with the math that actually matters, applied to real robotics problems from day one.
This is what open education looks like when a top-10 engineering school decides to mean it.
Free. GitHub. YouTube.
📌 [https://t.co/3STu1hzAz2]
Follow for more robotics resources!
——
Weekly robotics and AI insights.
Subscribe free: https://t.co/9Nm01QUcw3
⚡️Satya is describing the new balance sheet of the firm.
The old firm owned people, processes, software, customer relationships, brand, data, and IP.
The new firm will own a compounding cognition loop.
Every workflow becomes a training surface. Every decision becomes a trace. Every expert judgment becomes reusable signal. Every internal correction becomes model improvement. Every model run becomes a chance to turn human judgment into institutional intelligence.
That is what “token capital” really means.
It is accumulated machine-operable cognition. A company’s expertise becomes executable, queryable, evaluable, improvable, and portable across models.
That is a massive shift.
The most important line is the one about switching out the generalist model without losing the company veteran expertise. That is the entire enterprise AI war. Model providers want the firm’s knowledge to flow into the model layer.
Enterprises need that knowledge to stay inside their own loop.
Whoever owns the loop owns the future economic rent.
Satya is laying out Microsoft’s answer to the frontier-model monopoly problem.
If all company knowledge flows upward into a few foundation models, the foundation model labs become landlords of the entire economy. They absorb everyone’s expertise, commoditize every workflow, and capture the value created by every firm’s learning process. That equilibrium will trigger political backlash, customer resistance, regulatory pressure, and corporate revolt.
So Microsoft’s doctrine is: every company should build its own AI learning system on top of frontier models, while Microsoft owns the infrastructure where that happens.
That is elegant and self-serving.
Microsoft does not need to own the single best frontier model forever. It needs to own the enterprise control plane: identity, security, permissions, data, workflow, evals, agents, memory, developer tools, cloud, compliance, and model routing. If the model becomes swappable, the platform underneath the firm’s learning loop becomes the durable asset.
Satya is quietly saying the frontier model alone is unstable. A world of a few models eating every company’s expertise breaks the political economy. A world where every company builds firm-specific AI capital on top of models is more stable, more defensible, and much better for Microsoft.
The “human capital gets more valuable” line is partly true and partly corporate diplomacy.
High-agency humans become more valuable. People with taste, judgment, relationships, domain intuition, ambition, and the ability to direct agentic systems become much more valuable.
Routine cognitive labor loses bargaining power.
The future firm does not need every human equally. It needs humans who can generate high-quality signal for the loop. The human becomes a trainer, judge, strategist, relationship node, taste layer, and goal-setter. The work that cannot feed the loop or direct the loop gets compressed.
This also connects directly to the Anthropic crisis.
If frontier model access can be restricted, pulled, nationality-gated, or subordinated to state power, then enterprises cannot allow their intelligence layer to live entirely inside one external model. They need portability. They need private evals. They need internal memory. They need their own traces. They need model-agnostic learning systems.
The model can change.
The firm’s cognition loop has to survive.
That is the new sovereignty test.
A company that only buys AI access is a renter.
A company that turns its workflows, judgments, corrections, and outcomes into a private learning loop is building capital.
The deeper implication: the future economy splits between firms that compound cognition and firms that leak cognition.
Firms that compound cognition will get stronger every time they operate.
The most interesting thing in tech: Commerce bans foreign nationals—including Anthropic's own staff!—from using Fable. So the company shuts it down for all. Anthropic keeps warning AI is existentially dangerous but then keeps shipping. The government wants AI built here but incoherent regulations just derailed a leading product. It's not going well, folks.
David Sacks on How Anthropic is Ironically Running Surveillance on Their Latest Models
“This is the company that said that it was against government surveillance. They are now retaining for 30 days every prompt and every output you send to one of these Mythos class models.”