TechLead Bits
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About TechLead Bits
TechLead Bits is a Telegram channel dedicated to Technology, with 488 subscribers. About software development with common sense. Thoughts, tips and useful resources on technical leadership, architecture and engineering practices. Author: @nelia_loginova Join the channel to receive all updates directly on Telegram.
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About software development with common sense. Thoughts, tips and useful resources on technical leadership, architecture and engineering practices. Author: @nelia_loginova
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Skill Packaging
Engineering teams are actively building internal collections of skills for agents: code review, troubleshooting, design preparation, onboarding, security practices.
And it looks great until you hit the question:
how do you distribute those skills across dozens of teams and multiple harnesses?
For Claude you need to put skills into
.claude
, for Cursor into
.cursor
, for Gemini into
The Fearless Organization
The most dangerous teams are the quiet teams. There are no disagreements, no bad news, no conflicts. Looks like harmony until the real incident.
That's the topic of the book
The Fearless Organization: Creating Psychological Safety in the Workplace for Learning, Innovation, and Growth
by Amy C. Edmondson.
Amy is a professor of Leadership and Management at the Harvard Busi
Agent Readiness Framework
A few weeks ago I
wrote
that
adopting coding agents requires strong engineering practices
.
Test stability, linting, documentation, security controls matter much more than a particular harness or model.
Agent Readiness
framework is an attempt to formalize these criteria for a particular repository and define how much autonomy can be safely delegated to agents.
The framewo
How Anthropic Writes Skills
Last week
Anthropic published lessons learnt
of how they build agent skills internally. It's quite interesting to read recommendations from the company that introduced the concept in the first place.
Key ideas:
🔸
Don't be obvious.
Model already knows how to code. A skill should provide instructions that change default agent behavior, not repeat the data the model was tr
Project Hail Mary
Technical books and articles are great, but sometimes my brain needs a break. Especially now, when AI is generating more and more new things to learn every day. One of my favorite
ways to recharge is reading fiction
, and I recently finished the very popular
Project Hail Mary
by Andy Weir.
I'm not a big sci-fi fan, but I definitely enjoyed this book.
Thanks to the recent movie ad
"AI won't take your job. Someone using AI will."
This quote caught my attention and made me watch
A Leader’s Guide to Advanced Team Structures in an Agentic World
from the recent AWS Summit Sydney.
It's a very
sobering talk on the current state of the industry
, AI adoption, and the future of engineering roles.
The central question of the talk is: "
How should we build teams to work in this new AI
A Few Words About Context
New major model releases regularly promise bigger context windows. Sounds great until you realize it's mostly marketing. A bigger context window doesn't mean better results. It often means more data, more noise, and more AI slop.
According to multiple studies, models effectively
use only about 30–50% of their available context.
For example, a model with a 200K-token conte
Akrites Project
Over the past year, we've seen how much the
industry depends on small open source projects
. In many cases, software used by thousands of companies is maintained by one or two people working on it in their free time.
The release of the Mythos and Fable models made the situation even worse. They demonstrated how many vulnerabilities AI can find across thousand of projects, putting t
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