Olivia
Online
some clean and banger hero designs.
Si te interesa el SEO de tu web, esto es para ti.
OpenSEO tiene keywords, rankings, backlinks, auditorías y MCP para agentes de IA.
De código abierto y gratuito:
https://t.co/xHLYO6L1t1
img2threejs v1.3 is now available. 🎋
One photo → procedural Three.js code. No meshes. No manual modeling.
GitHub: https://t.co/0bqf4iS9rm
v1.3 brings major improvements to geometry reconstruction, material generation, validation, and overall output quality.
For this demo, I used the reference image of a CS2 ★ M9 Bayonet | Doppler Phase 3: https://t.co/8DflDWTPJ7
If you’re new to img2threejs, now is a great time to try it. If you’ve used it before, I’d love for you to compare the results with previous versions.
Feedback, bug reports, and PRs are always welcome.
Anthropic engineer:
“80% of our engineers are using self‑improving loops. Now everyone is building agentic Graphs.
In 4-6 months, we’ll all be building graphs to orchestrate self‑improving agents. No more prompting.”
in a 20‑minute talk, Anthropic engineer explains how to build self‑improving agentic systems from scratch.
Worth more than a $500 agentic course.
Watch this video, then read the article below on how to become a graph architect.
Somebody turned GitHub profiles into FIFA cards and I'm obsessed 😭
I just took this screen recording and turned it into a full product demo video in 20 minutes, using only one app.
3D animations, text, AI voiceover, music, and 3D gradient callouts to draw attention to the important parts.
But sure, keep posting Loom videos.
Met a solo founder who just raised $2 million from tier 1 VC.
No team. No cofounder. Only Claude Code & a laptop.
I asked him how he runs the whole company alone.
He sent me a 1 hour video that made me realize I've not been using Claude Code at even 5% of its potential.
Claude Code has 96 slash commands. I hardly knew 3.
This guy uses 60. Runs loops that ship features overnight. Runs Claude from his phone while he sleeps.
He teaches exactly what makes you ship 10x faster.
Bookmark & watch this. Then read the article on loop engineering below.
Ex-Google engineer explains how to build memory for self-improving AI agents in 12 minutes.
procedural (how to act / skills) + semantic (durable facts / profile) + episodic (dated events / chat history).
Memory is an important part of an agentic loop.
Memory + loops + harness + evals = self-improving agent system.
This is better than $500 paid courses on the internet - in under 12 minutes.
Watch it today, then save the detailed guide on building agent memory from scratch
需要跟同事讲解项目系统架构，光说不画图效果有限，自己动手画又费时间还画得不好看。
archify，一个能装进 Claude Code、Codex CLI 和 opencode 的 Agent Skill，把一段大白话描述直接变成一张架构图。
能画系统架构图、工作流程图、时序图、数据流向图和生命周期状态图这五种技术图，深色浅色主题一键切换。
GitHub：https://t.co/wlD7Os8d1u
生成的是单个自包含的 HTML 文件，不装额外依赖打开浏览器就能看，图能直接复制粘贴到 Slack 或 Notion 里。
也能导出到 4 倍分辨率的 PNG、JPEG、WebP，或者矢量 SVG。
经常需要跟同事讲清楚架构、写技术文档配图的朋友，用 Claude Code 顺手就能画，比手动画图省不少事。
Meetily is a local AI meeting assistant for capturing, transcribing, and summarizing meetings.
https://t.co/esMyLlsXJQ
i don’t even know how many startups just died after this
Anthropic engineer:
"You can build 5 assistants in one afternoon. Each one handles a task you've been doing manually every single day."
In 45 minutes he shows exactly how to do it from scratch, step by step.
Most people are still doing all of this by hand.
Watch the session, then save the guide below.
Generates 3D architecture diagrams
https://t.co/8kLxHrY9yE
“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]
![AndrewYNg's tweet photo. “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]](https://pbs.twimg.com/media/HMEtxp3bsAARJdi.jpg)
/writing-great-skills is quickly becoming my most often-invoked skill
It's just really good at writing skills, guys.
npx skills add mattpocock/skills --skill writing-great-skills
You don't understand how BIG this is.
Until now, agents could search the web but X was basically a wall. Real-time posts, trending topics, what people are actually saying right now, none of that was easy to pull into an agent workflow without a complicated API setup.
X just shipped a hosted MCP that changes that. Connect Grok, Cursor, or any MCP-compatible tool to the X API with no setup at all.
What this actually means if you run agents:
→ Your content agent can watch what's trending in your niche and surface it before you open your feed
→ Your research agent can pull real-time reactions to a product launch, not just news articles
→ Your morning brief now includes live X signal, not just web search
I run a content operation across 5 businesses. The bottleneck was always "my agent doesn't know what's happening right now." That just got solved.
If you're running agents and not wiring this in this week, you're leaving real-time context on the table.
As engineering, product, design, DS, etc. melt into a new kind of role, I was reflecting on what roles might look like in the future. For example, when I look at the Claude Code team I see what I think is five archetypes:
1. Prototyper: comes up with brand new ideas; churns out many ideas, most of which don't ship
2. Builder: quickly turns a prototype/idea into production-grade product/infra
3. Sweeper: cleans up the UI, simplifies the code and system, unships, optimizes performance
4. Grower: takes a product that has been built and iterates on it to improve Product-Market Fit
5. Maintainer: owns a mature system to make it secure, reliable, fast, and efficient as it scales
Many people span across 2 roles, and sometimes 3 roles. I also notice that these roles are not really tied to job function -- eg. across Anthropic, some designers match category 1, some 2, some 3; same for engineers, PM, DS.
A healthy team needs a mix of these, depending on the product:
- A product that is new and pre-PMF needs people that are strong at 1+2+3
- A product that is growing and has found PMF needs 2+3+4 and some 5
- A product that has strong PMF needs 3+4+5 and some 2
Maybe product roles of the future will look more like this, and less like the domain-specific roles of today?
Turn your images and videos into ASCII art 👾
❋ https://t.co/Mx8bM59UAH · asciilab launched!
The biggest AI skill of 2026 isn't prompt engineering.
It's AI literacy.
Because the people getting ahead aren't the ones with better prompts.
They're the ones who understand what's happening under the hood.
Harvard just made 5 AI courses free:
→ Introduction to Generative AI
https://t.co/f0LeTohQ6e
→ Prompt Engineering
https://t.co/FTxjz89eri
→ System Prompts & RAG
https://t.co/0M0zVaFF0d
→ Basics of Generative AI
https://t.co/iQycekaJec
→ CS50's AI with Python
https://t.co/EwO1FBUs7z
Most people won't take these.
That's exactly why they're valuable.
Bookmark it
Useful apps, sites for designers. Save this 👇
- https://t.co/nTHBtNiwJN - showcase designs in motion
- https://t.co/I1XzPxLAUm - 3D device mockups in browser
- https://t.co/2w6rURzzz1 - turn photos into recap video
- https://t.co/ilTH3ph2l1 - lo-fi effects for image & video
- https://t.co/gUDbXEHZqS - curve-based gradient creator
- https://t.co/YQIzFk40ZJ - infinite unique gradients
- https://t.co/WgNojNaEu2 - dither, ASCII & halftone effects
- https://t.co/YLrsjtrNhs - discover creatives on 𝕏, hourly
- https://t.co/OPC3oYmGIe - best of recent design, daily
What am I missing?
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