hhv

Proud $SUI Holder

[hhv @RedLion297]

@Crypto_Guerrila $SUI all days

[codila @0xCodila]

Google just released free 2-hour course on full agent engineering: 1 prompt → agent teams → loops → graphs from 0% to 100%:

10% → 38:46 - build your first agent
30% → 54:46 - connect MCP tools
55% → 1:12:43 - Loop engineering: 4 ways to run agents
70% → 1:20:57 - Graph engineering
100% → 2:22:31 - the full system that works while you sleep

most people build one agent and stop there - this is the full path to a system that runs without you from scratch

watch it today - then read the full graph engineering playbook below ↓

[Roan @RohOnChain]

Don't waste 2 years learning to build AI agents.

An Anthropic engineer who built Claude Code tells you what to learn from scratch instead.

60 minutes course. Free:

00:00 - AI agent architecture
24:47 - LangGraph AI agent
29:15 - building AI agents live

Prompting is the old job. Building AI agent loops is the new one.

Bookmark now & watch it. Then build your own AI agent with the guide below.

[rari @0xwhrrari]

https://t.co/9qtKcgOtPH

[Lunar @LunarResearcher]

Anthropic engineer just released a free 2-hour workshop.

How to build agentic graphs from scratch:

04:43 - RAG and graphs from zero
26:30 - Nodes, edges, and graph engineering
1:09:11 - Index agent data in graphs
1:30:50 - The three graph layers behind agents
1:48:15 - Build adaptive RAG that verifies itself
2:16:37 - The future of graph engineering

Most people build agents as isolated loops.

Graphs give them structure, memory, and control.

This workshop is worth more than most $500 agent engineering courses.

Bookmark and watch it today

Then read the full graph engineering guide below

[Movez @0xMovez]

Anthropic engineer:

“80% of our engineers are using self-improving loops. Now everyone is exploring how to build the right Context for agents.”

in 3-6 months, we’ll all become context engineers. Context is the most important part of building agents.”

In this 40-minute lecture, an Anthropic engineer shows how to build a self-improving agentic workflow from scratch.

It’s worth more than a $500 agentic course.

Watch this video, then read the article below on how to become a context architect.

[darkzodchi @zodchiii]

A senior Anthropic engineer just dropped 15-page PDF on "Graph Engineering and Agent Memory" for multi-agentic systems.

The shift: your agent's memory dies with its context window. A knowledge graph makes it permanent.

Extract → Store → Retrieve → Evolve

Every graph-based memory runs 4 stages:

• Extract: pull entities and typed relations out of raw docs and conversations into structured triples.

• Store: canonical nodes, typed edges, provenance on every fact. One connected graph instead of scattered chunks.

• Retrieve: multi-hop questions become graph traversal. "Who owns what breaks if this ships" is one walk, not six guesses.

• Evolve: the stage everyone under-builds. Facts get validity windows. Nothing is deleted, only marked superseded, so "who owned this in April" still answers.

This 15-page PDF changed how I'm building multi-agent systems today.

Read it now, then explore the article below👇

[unicode @unicodef1wn]

Boris Cherny, Head of Claude Code, just published a free graph engineering system built specifically for Opus 5

Opus 5 already matches Fable 5's performance at half the cost, $5 per million input tokens, 58 tokens/sec.

Stack a real graph on top, router, index, edges, one folder, and the same vault that used to burn hundreds of thousands of tokens per answer now runs on 2-3 files.

The PDF: Graph Engineering, Opus 5 Edition. 11 steps, zero fluff.

Save this and check out the article below 👇

[Neo Kim @systemdesignone]

If you want to become world-class with AI agents, then watch these 10 videos (save them):

[Neo Kim @systemdesignone]

1 AI Engineering

Full stack of building with foundation models & agents

[Neo Kim @systemdesignone]

2 Agentic Artificial Intelligence

Explains how AI agents reinvent business & work

[Neo Kim @systemdesignone]

If you want to become best in class with AI agents, then read these 12 guides (save now):

[Lunar @LunarResearcher]

Anthropic just dropped a free 4-hour Claude engineering course

The closest thing to their internal playbook:

00:15 - How Anthropic engineers prompt Claude
33:21 - The reason Claude gives worse outputs
01:33:39 - Their daily Claude workflow
02:50:56 - The tweak that unlocks better results

This replaces hours of random AI tutorials online

Most people are still asking AI questions.

The best engineers are building systems.

Bookmark and watch it today

Then read the guide below

[rari @0xwhrrari]

Claude Code team just built an agent loop system of 18,000 agents

140,000 steps and 90% of the work runs with zero humans typing

02:11 - From API to managed agents
04:33 - Ship agents 10-15x faster
08:48 - Cut P95 latency by over 90%
24:33 - Claude finds the root cause
33:04 - Subagents, memory, dreaming, and vaults

This one video is worth more than any paid agentic AI course

Bookmark and watch it tonight

Then read the article below

[Khairallah AL-Awady @eng_khairallah1]

A senior Google engineer just dropped a 424-page doc called Agentic Design Patterns.

Every chapter is code-backed and covers the frontier of AI systems:

→ Prompt chaining, routing, memory
→ MCP and multi-agent coordination
→ Guardrails, reasoning, planning

This isn't a blog post. It's a curriculum. And it's free.

Read it this weekend.

That said, I wrote an article on how to build a multi-model AI team in 2026, which was already read by 4 million people.

It's a complete guide to agents, models, workflows, and playbook to run your business on autopilot.

The article is quoted below.

[Miles Deutscher @milesdeutscher]

This is the one GitHub repo that everyone needs to save.

The CEO of Obsidian literally open-sourced his entire Claude Skills vault.

It's his personal Obsidian vault template, and it's built to 10x your AI productivity.

To get started:

1. Download the vault in the GitHub link below

2. Unzip the .zip file (Claude can help here)

3. Open Obsidian & create a new vault pointing to that folder

→ https://t.co/2Gvptnp4gQ

[Claude @claudeai]

Beginning July 20, Claude Fable 5 will be included in all Max and Team Premium plans, at 50% of limits.

Pro and Team Standard users will continue to have access to Fable via usage credits, and will receive a one-time $100 credit.

Demand for Fable has been challenging to predict, which is why we rolled it out to subscription plans in stages, extending access several times as we secured additional capacity.

[Boris Cherny @bcherny]

I talk to engineers at other companies every day and hear the same thing: one person is 10x'ing their output with Claude but the rest of the org hasn't caught up.

Watching teams adopt AI, I keep seeing the same 4 steps.

I mapped them out here: Steps of AI Adoption https://t.co/kQnRAUMKpP

[Thariq @trq212]

great post by @delba_oliveira

[Andrew Ng @AndrewYNg]

“Loop engineering” is a hot buzzphrase after mentions of it by Boris Cherny (Claude Code’s creator) and Peter Steinberger (OpenClaw's creator) went viral on social media. Loops are now a key part of how we get AI agents to iterate at length to build software. In this letter, I’d like to share my 3 key loops, shown in the image below, for building 0-to-1 products. These loops guide not just how I build software, but also how I decide what software to build.

Agentic coding loop: Given a product specification and optionally a set of evals (that is, a dataset against which to measure performance), we can have an AI agent write code, test its work, and keep iterating until the code is bug-free and meets its specification. This idea of closing the loop took off around the end of last year, and it has been a game changer in enabling coding agents to work longer productively without human intervention. For example, over the weekend, I was building an app for my daughter to practice typing, and my coding agent could easily work for around an hour, using a web browser to check what it had built multiple times before getting back to me, without needing my intervention.

The engineering loop executes quickly. Every few minutes, the coding agent might build and test a new version of the software. I hear frequently from developers who are finding new ways to engineer more effective engineering loops. This is an active area of invention!

Developer feedback loop: In this loop, a developer examines the current product and steers the coding agent to improve it. Last year, a lot of developers (including me) were acting as the QA (quality assurance) function for our coding agents, manually finding bugs and then asking the agent to fix them. But with coding agents much more able to test their own code, the amount of time we need to spend on this function has decreased significantly. This allows us to make higher-level product decisions, such as what key features to offer, where the UI needs improvement, and so on.

The developer-feedback loop operates over time intervals between tens of minutes and hours — that's how frequently a developer might review a product and give feedback. In the case of the typing app, I changed my mind a few times about the visual design, what cat costumes she can unlock as she learns (she loves cats), and the user flow for a grown-up to log in and steer the child's learning experience.

When a developer has a clear vision for what to build, it is still a lot of work to translate that vision into a specification for a coding agent to implement. Further, after the developer has seen an implementation, they might update (or perhaps clarify) the spec to steer it toward what they want. If you find that the system repeatedly runs into certain problems, building a set of evals for the agent becomes useful.

AI-native teams are increasingly using AI to help shape product direction, for example, automating the gathering and analysis of usage data, summarizing written and verbal customer feedback, or carrying out competitive analysis. However, for pretty much all the products I’m involved in, I see humans as having a significant context advantage over current AI systems — we know a lot more than the AI system about the users and the context the product has to operate in — and thus humans play a critical role. Many people describe this human contribution as “taste,” but I prefer to think of it as humans having a context advantage, since that gives us a clearer path to helping AI systems get better. This also speaks to why this step can’t be automated: So long as the human knows something the AI does not, human-in-the-loop is needed to to inject that knowledge into the system.

External feedback loop: This includes a wide range of tactics like asking a few friends for feedback, launching to alpha testers, or putting the code into production with A/B testing. These tactics are usually slow, rarely taking less than hours and sometimes taking days or even weeks. This data informs the developer vision, which in turn continues to drive the detailed product spec, which in turn drives the coding agent.

With coding agents speeding up software development, more engineers are starting to play a partial product management role. For many engineers who are growing into this role, the hardest part is shaping the product vision and striking a balance between building (bridging the gap between vision and spec) and getting user feedback to evolve the vision. It is important to do both!

I will write more about how to do this in future posts, but for now, I find it encouraging that engineers are playing an expanded role (just as product managers and designers now do more engineering).

[Original text: The Batch]