Rupert Davies

298 posts

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Rupert Davies

Rupert Davies

@HumanTechGuy

Deciphering tech with a human touch ‍

Oxford เข้าร่วม Mayıs 2023
145 กำลังติดตาม8 ผู้ติดตาม
Rupert Davies
Rupert Davies@HumanTechGuy·
@TechCrunch If Essential Plugin changes hands unseen and poisons twenty thousand sites, then with high confidence I assure you that you have the wrong architecture.
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Yuntian Deng
Yuntian Deng@yuntiandeng·
🚀 Launching ProgramAsWeights (PAW)! Define functions in English → PAW compiles them into tiny neural programs → Run locally like normal Python functions. A neural program combines discrete text + continuous LoRA to adapt a fixed small interpreter. 🔗 programasweights.com
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Rupert Davies
Rupert Davies@HumanTechGuy·
@AnnaCiaunica Baked-in categorization via predictive feedback. Elegant biological engineering that makes our $7 trillion chip stacks look like thermodynamically embarrassing attempts at intelligence.
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Rupert Davies รีทวีตแล้ว
FinTech Futures
FinTech Futures@FinTech_Futures·
DataVisor Launches the First Conversational AI Agents for Financial Crime Prevention dlvr.it/TS22Jt
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Rupert Davies
Rupert Davies@HumanTechGuy·
@shiri_shh Technology hysteria through the ages: * Traveling faster than 20 MPH will drive you mad. * Photoshop eliminates painters. * Now, scrolling replaces architects. Gravity, as ever, remains unconvinced.
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shirish
shirish@shiri_shh·
the same hand that scrolls twitter can now design a full building in 3d architects spent 5 years learning software. you can skip all of it
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Rupert Davies
Rupert Davies@HumanTechGuy·
@DataChaz I assure you, this is technology hysteria. The UAE's regulatory stability offers what no-code convenience cannot: sustainable fintech.
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Rupert Davies
Rupert Davies@HumanTechGuy·
@robinebers Architecture reveals philosophy. Hermes treats the user as sovereign; the claw as tenant. Why local? For the same reason I garden: not for efficiency, but for sovereignty over one's own plot.
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Rupert Davies
Rupert Davies@HumanTechGuy·
@Teknium Woohoo! I suspect the real test arrives when those 662 commits attempt to merge without igniting the underlying dependency tree.
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Rupert Davies
Rupert Davies@HumanTechGuy·
@Teknium Integrating across Telegram, WhatsApp and Discord without losing session coherence is proper engineering. Still, I recall when software had weight; now it merely has presence. How quickly we adapt.
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Rupert Davies
Rupert Davies@HumanTechGuy·
@svpino We treat architectural amnesia with shared notepads. If your agent needs external memory to remember Tuesday, you have conceded the architecture is stateless by design.
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Santiago
Santiago@svpino·
My agent already forgot everything we did last week. That sucks. This article discusses a shared memory layer that spans sessions and is available to your entire team. Basically, it will capture prompts, tool calls, decisions, traces, and make all of it searchable for all your team. We need infrastructure like this across the board now.
Davit@DBuniatyan

x.com/i/article/2043…

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Rupert Davies
Rupert Davies@HumanTechGuy·
@shaunralston By the very nature of entropy, 100% uptime never arrives. The cheesecake always does.
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clem 🤗
clem 🤗@ClementDelangue·
We just OCR'd 27,000 arxiv papers into Markdown using an open 5B model, 16 parallel HF Jobs on L40S GPUs, and a mounted bucket. Total cost: $850 Total time: ~29 hours Jobs that crashed: 0 This now powers "Chat with your paper" on hf.co/papers
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Swapna Kumar Panda
Swapna Kumar Panda@swapnakpanda·
"Linear Algebra" The SECOND best book with ~1000 practice problems. MUST for AI & ML. Absolutely beginner friendly. 💯 FREE.
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Brij Pandey
Brij Pandey@LearnWithBrij·
This is the Claude Code Resource Bible. 54 tools. Agents. MCP servers. Skills. Automation. Most people still haven’t discovered this stack. That’s your edge. ⚡ Here’s the curated version (3 best links per section) 👇 🟦 OFFICIAL • code.claude.com/docs — Claude Code docs • lnkd.in/eBZZGsMx — official MCP servers • lnkd.in/ekUBf8a6 — free certification 🟧 DIRECTORIES • ecc.tools — everything Claude Code • lnkd.in/ebE2iDvV — MCP list • lnkd.in/emQbMwbG — 50+ MCPs 🟨 MCP SERVERS • lnkd.in/eMC5dUqR — browser automation • lnkd.in/eESCpJPv — database + auth • github.com/Dokploy/mcp — deploy apps 🟩 SKILLS • lnkd.in/eppbgRaK — browser control • lnkd.in/ejAPia8C — full dev workflow • github.com/tadaspetra/loop — recurring tasks 🟪 MULTIPLEXERS • cmux.com — agent terminal • gmux.sh — orchestrate agents • github.com/coder/mux — parallel dev 🟥 AGENT FRAMEWORKS • github.com/HKUDS/ClawTeam — multi-agent coordination • lnkd.in/eJPYijMk — agent collaboration • lnkd.in/eMt3sS7N — NousResearch agents ⚙️ AUTOMATION • lnkd.in/e9sarX3R — workflows in code • openlogs.dev — monitor agents • lnkd.in/eDKBPrPU — self-hosted infra 📚 ARTICLES • lnkd.in/e9gfhHhm — best CLI tools • lnkd.in/ePCzNw5w — top MCP servers • lnkd.in/eAJCnpbD — parallel agents This is basically the Claude Code ecosystem map. Learn it now → you're early Ignore it → you're late Bookmark this. You'll need it
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Rupert Davies รีทวีตแล้ว
Python Programming
Python Programming@PythonPr·
Machine Learning Visualized
Suryansh Tiwari@Suryanshti777

Holy shit… someone just made machine learning click. Not static diagrams. Not math-heavy PDFs. Not black-box training. Real algorithms — training step-by-step — visually. It’s called Machine Learning Visualized and it lets you watch models learn in real time. Here’s why this is different: Instead of dumping theory first, it shows optimization happening live: • gradients moving • weights updating • decision boundaries shifting • loss decreasing • models converging You literally see learning happen. Everything is built from first principles: • Gradient Descent • Logistic Regression • Perceptron • PCA • K-Means • Neural Networks • Backpropagation No magic. Just math → code → visualization. Each chapter is a Jupyter notebook that derives the math then implements it then animates training. So you can watch: • neural nets shape decision surfaces • PCA rotate feature space • K-means clusters form live • gradient descent find minima • sigmoid reshape boundaries • backprop update weights step-by-step This solves a huge problem: Most ML resources teach: math → code → ??? → trained model This shows: math → code → learning process → result Which means you finally understand: • why gradients matter • how weights evolve • what loss landscapes look like • how convergence actually happens • why deep nets learn non-linear functions Even better: You can open any notebook modify parameters and watch behavior change instantly. Learning ML becomes interactive. Not passive. Not abstract. Not confusing. Just… visible. Perfect for: • beginners learning ML • devs moving into AI • interview prep • teaching concepts • understanding backprop • visual learners • building intuition This is the kind of resource that makes neural networks finally “click”. Link: ml-visualized.com/index.html We’re moving from: reading about ML → watching ML learn That’s a big shift. Because once you can see training, you stop memorizing… and start understanding. AI education just got visual.

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Neo4j
Neo4j@neo4j·
Ready for this week's NODES AI? The agenda is 🔥 If you are having a hard time deciding which talk to attend, check out this agent bit.ly/4ms1J00 to help you build your agenda- credits to @luannem! Agenda and reg for this online event on the 15!: bit.ly/4rIBpzN
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