Join us

ContentUpdates and recent posts about INTELLECT-3..
Link
@faun shared a link, 9 months, 3 weeks ago
FAUN.dev()

Everything I know about good API design

This guide lays out the playbook for running tough, user-first APIs: no breaking changes, stick to familiar patterns, honor long-lived API keys, and make every write idempotent. It pushes cursor-based pagination for heavy data, rate limits that come with context, and optional fields to keep things .. read more  

Link
@faun shared a link, 9 months, 3 weeks ago
FAUN.dev()

Open Source is one person

New data from ecosyste.ms drops a hard truth:almost 60% of 11.8M open source projects are solo acts. Even among NPM packages topping 1M monthly downloads, about half still rest on one pair of hands. The world runs on open source. But the scaffolding seems shakier than anyone wants to admit—millions.. read more  

Open Source is one person
Link
@faun shared a link, 9 months, 3 weeks ago
FAUN.dev()

From Python to Go: Why We Rewrote Our Ingest Pipeline at Telemetry Harbor

Telemetry Harbor tossed out Python FastAPI and rebuilt its ingest pipeline inGo. The payoff?10x faster, no more CPU freakouts, and strongerdata integritythanks to strict typing. PostgreSQL is now the slowest link in the chain—not the app—which is the kind of bottleneck you actuallywant. Means the s.. read more  

From Python to Go: Why We Rewrote Our Ingest Pipeline at Telemetry Harbor
Link
@faun shared a link, 9 months, 3 weeks ago
FAUN.dev()

Bash Explained: How the Most Popular Linux Shell Works

Bash isn't going anywhere. It's still the glue for CI/CD, cron jobs, and whatever janky monitoring stack someone duct-taped together at 2am. If automation runs the show, Bash is probably in the pit orchestra. It keeps things moving on Linux, old-school macOS (think pre-Catalina), and even WSL. Stil.. read more  

Link
@faun shared a link, 9 months, 3 weeks ago
FAUN.dev()

Go is still not good

Go’s been catching flak for years, and the hits keep coming: stiff variable scoping, no destructor patterns, clunky error handling, and brittle build directives. Critics point out how Go’s design often blocks best practices like RAII and makes devs contort logic just to clean up resources or manage .. read more  

Link
@faun shared a link, 9 months, 3 weeks ago
FAUN.dev()

Lessons learned from building a sync-engine and reactivity system with SQLite

A dev ditched Electric + PGlite for a lean, browser-native sync setup built aroundWASM SQLite,JSON polling, andBroadcastChannel reactivity. It’s running inside a local-first notes app. Changes get logged with DB triggers. Sync state? Tracked by hand. Svelte stores update via lightweight polling, wi.. read more  

Lessons learned from building a sync-engine and reactivity system with SQLite
Link
@faun shared a link, 9 months, 3 weeks ago
FAUN.dev()

Developer's block

Overdoing “best practices” can kill momentum. Think endless tests, wall-to-wall docs, airtight CI, and coding rules rigid enough to snap. Sounds responsible—until it slows dev to a crawl. The piece argues for flipping that script. Start scrappy. Build fast. Save the polish for later. It’s how you d.. read more  

Link
@faun shared a link, 9 months, 3 weeks ago
FAUN.dev()

From GPT-2 to gpt-oss: Analyzing the Architectural Advances

OpenAI Returns to Openness. The company droppedgpt-oss-20Bandgpt-oss-120B—its first open-weight LLMs since GPT-2. The models pack a modern stack:Mixture-of-Experts,Grouped Query Attention,Sliding Window Attention, andSwiGLU. They're also lean. Thanks toMXFP4 quantization, 20B runs on a 16GB consume.. read more  

From GPT-2 to gpt-oss: Analyzing the Architectural Advances
Link
@faun shared a link, 9 months, 3 weeks ago
FAUN.dev()

I set up an email triage system using Home Assistant and a local LLM, here's how you can too

A DIY email triage rig usingHome Assistant, IMAP, andOllamawires up local LLM smarts with YAML-fueled automation. At the core: an8B dolphin-llamamodel running on GPU, chewing through messy HTML emails, tagging them, and firing off priority-sorted summaries via notifications. Why it matters:A signal.. read more  

I set up an email triage system using Home Assistant and a local LLM, here's how you can too
Link
@faun shared a link, 9 months, 3 weeks ago
FAUN.dev()

The Most Important Machine Learning Equations: A Comprehensive Guide

A new reference rounds up the core ML equations—Bayes’ Theorem, cross-entropy, eigen decomposition, attention—and shows how they plug into real Python code using NumPy, TensorFlow, and scikit-learn. It hits the big four: probability, linear algebra, optimization, and generative modeling. Stuff that.. read more  

INTELLECT-3 is a frontier-class 100B+ Mixture-of-Experts language model developed by Prime Intellect and trained end-to-end using their large-scale asynchronous RL framework, PRIME-RL. Built on the GLM-4.5-Air base model, INTELLECT-3 combines supervised fine-tuning with long-horizon reinforcement learning across hundreds of verifier-backed environments spanning math, code, science, logic, and agentic tasks.

The model was trained on a high-performance cluster of 512 NVIDIA H200 GPUs across 64 nodes, supported by Prime Intellect’s Sandboxes execution engine, deterministic compute orchestration, and Lustre-backed distributed storage. The result is a model that surpasses many larger systems in reasoning benchmarks while remaining fully open-source.

Prime Intellect released not only the model weights but also the full training recipe: PRIME-RL, Verifiers, the Environments Hub, datasets, and evaluation suites. INTELLECT-3 is positioned as a foundation for organizations seeking to post-train or customize their own frontier-grade models without relying on proprietary AI labs.