Brent maybe try a closer camera angle in a nature background like

Nemesis system was actually AI. Checkmate Mister Zitron 🥲

Didn’t expect my F1 & AI interests to collide but 2026 battery regulations are forcing it! Here’s a fantastic explanation of narrow AI used for engine management by @eddstrawF1 that I just had to call it out as a correct way to articulate the topic: youtu.be/j0_Rb84XKgA?si=9yXR…

Recursive Self-Improvement (RSI) for software harnesses is having a moment this week with @PrimeIntellect’s self-improving Prime Agent and @sawyerhood’s great article on bb, and IDE that builds itself. Here are several more of the top articles I’ve been reading to learn about RSI for harnesses in particular: * lilianweng.github.io/posts/2… - Harness Eng for Self-Improvement is just a great in-depth article about harness design patterns, not tied to any specific implementation * metr.org/blog/2025-02-14-mea… METR’s measuring automated kernel engineering from early 2025 contains a lot of detail based on 4o-level models and shows how difficult it is to measure realistic tasks which are likely necessary for RSI’s feedback loops * normaltech.ai/p/ai-agents-ca… - not about harnesses specifically but a summary on a recent paper that covers what I’ve recently realized where agents are deciding based on data, and when the data isn’t available it’s a path not taken. Perhaps a “research harness” could course correct an AI model at the right time? Links to the articles I mentioned in the intro sentence: Prime Intellect’s article on Prime Agent: primeintellect.ai/blog/prime… Sawyer’s article on bb: xitter.cf/sawyerhood/status/2085… Image is from the Harness Engineering for Self-Improvement Post.

Loop engineering developer trends are now available in Plushcap. This trend tracks product features and content by dev tools companies for designing self-sustaining feedback loops around AI coding agents so they can re-prompt themselves, evaluate their own output, and iterate toward a goal without requiring a human to intervene at each step. "Ralph loops" and "Ralph Wiggum loops" are also included in this one, but not graph engineering, as that seems to be a closely related but still separate architectural branch for making AI coding tools more autonomous. Insights on loop engineering: plushcap.com/blog/loop-engin… Direct trend data link: plushcap.com/trends/monthly/… All of the data and insights are available as inputs to Claude/ChatGPT/etc via the Plushcap MCP server and API: mcp.plushcap.com

A few resources on local model coding agents (especially @googlegemma 4) that I'm really enjoying learning from recently: * simonpcouch.com/blog/2026-04… - 14-24B dense local models scored 0/10 on his refactoring eval but Gemma 4 26B-A4B jumped to 9/10. 26B parameters but MoE 4B active per token, so it's very fast. Matches my own experience on my 4090 RTX! * magazine.sebastianraschka.co… A great guide for wiring up local models to coding harnesses such as Claude Code, and goes into differences in models. For example, gemma4:e2b fails 0/5 on tool-reasoning tasks while Qwen 35B-A3B class solves 4-5/5 * patloeber.com/gemma-4-pi-age… - awesome walkthrough by @patlober that shows how to configure @pidotdev (which is also my local model coding harness of choice) with Gemma 4 * interconnects.ai/p/gemma-4-a… - argues that 30B parameter count is the number that matters (I disagree, 26B w/ MoE seems to be the floor for me) while 7B is for tinkering but overall a great read There seems to be a parameter floor in the ~26B model range below which local models just don't feel like as much of a breakthrough, and above that the best models like Gemma 4 and Qwen 3.x remind of of that "aha!" moment I had with Opus 4.5 early last year.

damn that’s an impressive subscribers trajectory: plushcap.com/companies/opena…

What an incredibly well-written, thoughtful, and informative article on data tools from a developer perspective, "Guide to data tools landscape for devs": sinja.io/blog/data-landscape… Reminds me of some of my other favorite technical posts in areas like storing & accessing data, how web browsers work, * queue.acm.org/detail.cfm?id=… for a sweeping view on progress in how storage is accessed and its behavior * howbrowserswork.com/ an interactive guide to how HTTP, DNS, SSL, TLS and all of the most important protocols work in the browser (TLS 1.3 is similarly great tls13.xargs.org/) * sinja.io/blog/bot-or-not - another great post from the same author but this one on bots & detecting them Amazing work @OlegWock, you deserve waaaay more followers and readers for your writings

Several articles & resources I’ve been reading lately to learn about how LLM architectures have evolved: * finbarr.ca/five-years-of-gpt… 5 years of GPT-class model progress, only goes to 2023 but that puts more focus on early models where details are precisely known * magazine.sebastianraschka.co… great companion post to the first one as its diagrams and code examples are absurdly helpful. @rasbt has so many articles that could be on this list but I picked this one because of the visuals in particular * gregorygundersen.com/blog/20… denser than other posts. my attention can glaze over some of the deeper math formulas but as a synthesis of key LLM concepts have evolved this is excellent * cameronrwolfe.substack.com/p… both accessible in the basics "what is a token?" and detailed, this one goes into other notable models from early LLM days (post image is from this article)

A few resources on harness engineering I'm really enjoying learning from recently: * walkinglabs.github.io/learn-… - excellent course that covers how to constrain agent behavior in a productive way, what the challenges are using LLMs especially maintaining context, and how to make the harness more agentic and work to the proper conclusion of a task * twotimespi.dev/ - build a simplified agent in Python (Tau). start with "uv tool install tau-ai" * humanlayer.dev/blog/skill-is… - lessons and learnings from failing and succeeding to build harnesses for coding agents over 12+ months * langchain.com/blog/the-anato… - higher-level overview of the many components that need to come together properly in a harness to make an agent work