Timothy Lee

[Khairallah AL-Awady @eng_khairallah1]

https://t.co/OjXCanEITW

[Machina @EXM7777]

https://t.co/BNuZ1GirbS

[ClaudeDevs @ClaudeDevs]

https://t.co/v694m8Eaj6

[Movez @0xMovez]

Anthropic just dropped 5 workshops on building self-improving agentic systems from scratch:

00:00 - Ship your first Claude agent
36:44 - Build memory for Claude agents
1:05:06 - Make your agent autonomous
1:26:46 - Set up a proactive agent
2:03:35 - self-improving agents (tools,skills)

These 3-hours of free Claude workshops will replace 10 paid agentic courses.

Watch today, then read article below on how to build a self-improving agentic system with Fable 5.

[vixhaℓ @TheVixhal]

https://t.co/qQKhudPYIL

[Khairallah AL-Awady @eng_khairallah1]

https://t.co/EQi4JlGoLQ

[Nitin.nn @NitinthisSide_]

If I had 90 days to become an AI Engineer in 2026, I'd do this.

1. Python - syntax, OOP, async, APIs.

2. ML Fundamentals - scikit-learn, evaluation, feature engineering.

3. Deep Learning - PyTorch, neural networks, transformers.

4. LLMs - prompting, embeddings, RAG, function calling, fine-tuning.

5. AI Agents - MCP, tool use, memory, workflows, multi-agent systems.

6. AI Stack - LangChain, LangGraph, Hugging Face, vector databases.

7. Backend - FastAPI, Docker, Redis, PostgreSQL.

8. Deployment - CI/CD, monitoring, cloud, GPU inference.

9. Build - RAG chatbot, coding agent, voice assistant, research agent, multi-agent system.

10. Portfolio - 5 production-ready AI products with live demos and GitHub.

11. Apply - AI Engineer, GenAI Engineer, Applied AI Engineer, AI Product Engineer.

Don't just learn AI. Build. Deploy. Ship.

[Phil Chen @philhchen]

https://t.co/oMoEAYkqGl

[DAN KOE @thedankoe]

https://t.co/gN9cBK7feV

[AI Edge @aiedge_]

One of the smartest things you can do with Hermes right now:

Use Obsidian to build a second-brain database that self-evolves over time.

Here's how to set it up in <5 minutes:

Step 1. Download Obsidian

Head to obsidian . md and download the desktopp app.

Create a new Obsidian vault (this is where all notes live locally).

Start dumping everything in here:

- Business context
- Personal goals
- Meeting summaries

The more you put in, the more powerful it gets.

Step 2. Connect Hermes to your Obsidian vault

Open the new Hermes desktop app and paste this prompt:

"I want to connect you to my new obsidian vault and have you act as my second-brain orchestrator - do everything necessary to set up that connection now."

This gives Hermes direct access to everything inside your vault.

Step 3. Let it self-evolve

Every time you add a new note, Hermes automatically ingests it.

Prompt Hermes:

"Every day, scan my second brain database. Every time I add a new note, use it to create reusable skills and workflows."

Pro tip: Once Obsidian is connected to your agent, you can just prompt it directly to add notes/context/data.

This is what a true AI second brain looks like.

Save this and build your second brain system now.

[Miles Deutscher @milesdeutscher]

https://t.co/O7x82o2AAf

[Daily Dose of Data Science @DailyDoseOfDS_]

A harnessed LLM agent, clearly explained!

Most people picture this as a model with tools bolted on. The real architecture inverts that relationship.

The model itself is deliberately thin. Intelligence gets pushed outward, and the harness composes it at runtime.

Three dimensions orbit the harness core:

- 𝗠𝗲𝗺𝗼𝗿𝘆 holds the state a model shouldn't carry in weights or context. Working context, semantic knowledge, episodic experience, and personalized memory each have their own lifecycle.

- 𝗦𝗸𝗶𝗹𝗹𝘀 hold procedural knowledge. This can cover operational procedures, decision heuristics, and normative constraints that specialize the general model per task.

- 𝗣𝗿𝗼𝘁𝗼𝗰𝗼𝗹𝘀 hold the interaction contracts. Agent-to-user, agent-to-agent, and agent-to-tools are three distinct surfaces with their own failure modes.

Between the core and these modules sit the mediators, like sandboxing, observability, compression, evaluation, approval loops, and sub-agent orchestration.

They govern how the harness reaches out and how state flows back in.

The useful question this framing unlocks is: for any new capability, where should it live?

- Stable knowledge goes to memory
- Learned playbooks go to skills
- Communication contracts go to protocols
- Loop governance goes to the mediators

Harness design becomes a question of what to externalize, and how to mediate it.

We wrote an article about the anatomy of Agent Harness, covering the orchestration loop, tools, memory, context management, and everything else that transforms a stateless LLM into a capable agent.

Read it below.

[Movez @0xMovez]

Ex-Google engineer explained AI agent loops, harness, evals in 20 minutes - better than 500$ courses.

trace every run → judge it with an LLM → diagnose → fix → ship.

That loop is how agents self-improve over time.

Agent loops + memory + harness + evals - thats the stack.

Watch it, then save the framework below.

[Ronin @DeRonin_]

My entire AI stack is now Chinese 🇨🇳

87% cheaper. same revenue

swaps by task:

1. reasoning / backend brain
Opus 4.8 → Kimi K2.7
benchmark gap: ~8% · price: ~11x cheaper

2. code generation
GPT-5.5 → Qwen 3.7 Max
benchmark gap: ~18% · price: ~7x cheaper

3. agent loops + tool calling
Sonnet 4.7 → GLM 5.2
benchmark gap: ~3% · price: ~5x cheaper on input

4. cheap volume / bulk processing
GPT-5.5 mini → MiMo V2.5
benchmark gap: ~6% · price: ~12x cheaper

5. image generation
GPT-Image-2 → Wan 2.5
benchmark gap: ~5% · price: ~8x cheaper

6. video generation
Sora 2 → Kling 3.0
benchmark gap: roughly equal · price: ~6x cheaper

[ result after 30 days: ]

operating costs dropped 87%, output quality dropped 4% on average, revenue unchanged

the most important that these models will be not banned in a month and i can run them locally

nobody will steal my data and i can learn them as i need

full article drops tomorrow with:
> exact routing logic per task type
> the 2 cases where I still pay for American
> the migration playbook anyone can copy in a weekend

VERY IMPORTANT to get migrated now, while it's not too late

[David Ondrej @DavidOndrej1]

start running deepsec on all your repos

trust me.

[Ruben Hassid @rubenhassid]

https://t.co/0j9ObTybve

[Akshay 🚀 @akshay_pachaar]

https://t.co/5lb52h4oT1

[Anatoli Kopadze @AnatoliKopadze]

Google just dropped a free 8-minute lesson on building your first AI agent.

This is the clearest explanation of AI agents and loops you'll find anywhere.

People are paying $500 for courses that teach less than this.

Watch it, then read the step by step guide on building loops for your agents below.

[GREG ISENBERG @gregisenberg]

The 6 MOST valuable skills to learn in the AGENTIC era and how to learn them (clearly explained in 29 minutes)

[Ronin @DeRonin_]

As an AI engineer in 2026, learn this:

> systematic output reading. pattern recognition across 1,000 model responses is the actual skill, not writing the 1,001st prompt

> context engineering. retrieval design, tool description quality, context window management. 90% of output quality lives here, not in the prompt

> tool description discipline. naming, params, when-to-call guidance. write them like documentation, not afterthoughts

> eval design as taste-building. golden sets, embarrassment-resistant scoring, LLM-as-judge calibration. evals aren't tests, they're how you develop AI judgment

> model routing logic. task-type classification, tiered selection, fallback chains. defaulting to one frontier model is the most expensive junior tell

> prompt versioning. git-style tracking, A/B testing in production, rollback strategy. every prompt is code, version it as code

> confidence scoring as a structured output. self-assessment, critic models, threshold routing. low confidence should auto-route to human review

> streaming response architecture. SSE patterns, partial UI rendering, perceived-latency optimization. loading spinners in 2026 are a UX tax

> fallback chain design. primary → backup → cheap → cached. no production AI feature ships without this

> latency budgets across user journeys, not per API call. one slow agent loop kills the flow even if individual calls are fast

> failure mode cataloguing. taxonomy of how your agents, prompts, and retrieval break. that notebook becomes your real moat by month 6

> agent-vs-workflow-vs-single-call decision framework. knowing when NOT to use an agent is more valuable than any agent framework

> failure post-mortems as portfolio content. engineers who get hired document real bugs they wrestled with, not demos they copied

the article covers the curriculum. these decide if you're actually good at this