Ways to Train an #LLM
by @goyalshaliniuk #GenAI #ArtificialIntelligence #MachineLearning #ML
LLMS
-
Ways to Train Large Language Models Effectively
By
–
-
JSON’s Token Inefficiency Problem Costs Real Money
By
–
json is so token inefficient it hurts these days man, these braces and quotes are costing me real $$
-
Lightweight AI Agent Processing Memory Continuously 24/7
By
–
An AI Agent that runs 24/7 as a lightweight background process, continuously processing, consolidating, and connecting information. Just an LLM that reads, thinks, and writes structured memory. Built with Gemini 3.1 Flash Lite.
-
Lightweight AI Agent Processing Memory Continuously 24/7
By
–
An AI Agent that runs 24/7 as a lightweight background process, continuously processing, consolidating, and connecting information. Just an LLM that reads, thinks, and writes structured memory. Built with Gemini 3.1 Flash Lite.
-
OpenClaw GPT-5.4 Update: Finding the Sweet Spot for Daily Use
By
–
OpenClaw with GPT-5.4 feels noticeably better with the new update. GPT-5.4 (thinking=high + fastmode=true) seems like the sweet spot. Better judgment, less back-and-forth, still fast enough for daily use.
-
OpenClaw GPT-5.4 Update: Finding the Sweet Spot for Daily Use
By
–
OpenClaw with GPT-5.4 feels noticeably better with the new update. GPT-5.4 (thinking=high + fastmode=true) seems like the sweet spot. Better judgment, less back-and-forth, still fast enough for daily use.
-
Comparing 26B A4B and 31B Model Performance
By
–
Have you tried the 26B A4B one? I've been impressed by it, I've not compared it to the 31B yet though
-
Claude.md File Reaches 15K Stars with AI Coding Guidelines
By
–
A single 𝗖𝗟𝗔𝗨𝗗𝗘.𝗺𝗱 file just hit 15K GitHub stars. (derived from Karpathy's coding rules) Andrej Karpathy observed that LLMs make the same predictable mistakes when writing code: over-engineering, ignoring existing patterns, and adding dependencies you never asked for. If you've used AI coding assistants, you've hit all of these. But here's the thing: If the mistakes are predictable, you can prevent them with the right instructions. That's exactly what this 𝗖𝗟𝗔𝗨𝗗𝗘.𝗺𝗱 does. You drop one markdown file into your repo, and it gives Claude Code a structured set of behavioral guidelines for your entire project. This is a big deal. – Built entirely around prompt engineering for AI coding assistants – No framework, no complex tooling, just one .md file that shapes behavior Developers are moving past "use AI to write code" and into "engineer the AI's behavior so the code is actually good." The Claude Code ecosystem is growing fast, and the best tools in it aren't always software. Sometimes they're just well-crafted instructions. 100% open-source. I've shared a link to the GitHub repo in the next tweet!
→ View original post on X — @akshay_pachaar, 2026-04-12 17:02 UTC
-
Top AI Papers: Agents, LLMs, and Coding Automation
By
–
The Top AI Papers of the Week (April 6 – 12) – Memento
– Neural Computers
– The Universal Verifier
– Agent Skills in the Wild
– Memory Intelligence Agent (MIA)
– Single-Agent vs Multi-Agent LLMs
– Scaling Coding Agents via Atomic Skills Read on for more: -
Why Claude’s Concise Style Makes It More Appealing
By
–
From my own experience, I can say that I'm using Claude more and more. It's simply the taste; the less "chatty" nature and the more concise answers make it more appealing.