The Code Newsletter

Learn how to code faster with AI in 5 mins a day. Get high signal AI software development news, research papers & trending resources. By @superhuman_ai

[The Code Newsletter @CodeNewsletter]

https://t.co/mL928Ju8Wj

[Andrew Ng @AndrewYNg]

OpenWorker -- an open source agent that doesn't just chat but completes tasks on your laptop -- just released a new version with many features for security workflows.

After our initial release, many users found it especially useful for cybersecurity. Attackers are already using AI; OpenWorker is committed to giving defenders the same leverage. Running an agent requires both (i) A model and (ii) A harness (the software around the model). Because the OpenWorker harness is fully open source, security teams can audit it to make sure we haven't built any backdoors that exfiltrate your code and data to some company or even a foreign adversary.

OpenWorker now comes with built-in cybersecurity agents for (i) Scanning your code for vulnerabilities. (ii) Scanning dependencies for supply chain injections. (iii) Checking your cloud security configuration for attack surfaces. This enables developers to do much more security work before deployment (part of what's called the "shift left" movement).

You choose the model: you can run open weight models fully locally so sensitive code never leaves your machine. This helps with legitimate security work (like reproducing a known exploit to defend against it) that can trigger refusals in leading closed models. Or use your ChatGPT subscription, or stealth preview models like Ox Alpha, or any model via API key.

Thanks also to all the open source contributors!

Join work with @rohitcprasad so please follow him too to get more frequent updates.

Try it out: https://t.co/QPZLudn7ug
Code: https://t.co/NYCiTD6hSq

[International Cyber Digest @IntCyberDigest]

Apple just released new Macs, and they’re a huge leap for local AI:

M6 Mac mini
• 12-core CPU / 12-core GPU
• 32GB unified memory
• 170GB/s bandwidth
• Up to 13.5× faster LLM prompt processing vs M1, 4.8× vs M4

M5 Ultra Mac Studio
• 36-core CPU / 80-core GPU
• 512GB unified memory
• 1.2TB/s bandwidth
• Up to 9.8× faster LLM prompt processing vs M1 Ultra, 4× vs M3 Ultra

[Rob Pruzan @RobKnight__]

Introducing terminal-code: VS Code inside the terminal

- VS Code compatible CLI
- works over ssh
- syncs with your terminal theme

https://t.co/j3HThPdVPw

[Puneet Patwari @system_monarch]

I've been a backend Engineer for 12+ years. Today, I'm a Principal Engineer at Atlassian.

I've designed systems that handle millions of requests. Sat on both sides of system design interviews.

Reviewed more architecture docs than I can count.

Starting today, I'm breaking down the fundamentals of scaling for the next 25 days.

If you're learning system design bookmark this thread, you're going to get a lot of learning from this.

[Jonata Santos @_jonatasantos]

how to build anything rn:

- get a hetzner, do, or hostinger vps
- host hermes on it
- add gbrain or implement your own memory vault using qmd + sql
- set up hermes with codex auth -> gpt-5.5 / no reasoning / fast mode
- install orca on your macbook and phone with tailscale to have a nice ide to work on both
- before starting any work, ask hermes to conduct deep research on the subject and save it to gbrain as source material for the project
- use the `/grill-me` skill or a similar prompt to uncover as many unknowns as possible. save results to memory too
- define/write clear evals for every project to determine whether a run was successful
- have hermes iterate over the project until all evals pass, saving all learnings to the vault along the way
- whenever it gets stuck, use memory + a new research or `/grill-me` session to unblock it

rinse and repeat until the work is done. pay attention to the process. develop a feeling for how long tasks should take and do not be afraid to stop a model mid session to ask for status and why it's taking so long.

[Gabriel @gabriell_lab]

Design Engineering Tip

Consider letting textareas grow with the content instead of introducing nested scrolling. It often creates a smoother writing experience and keeps forms easier to scan.

CSS:
textarea {
field-sizing: content;
}

[Emil Kowalski @emilkowalski]

To get good animations from an AI you need to get good at telling it what you want:

- "stagger this list of items"
- "make this animation direction-aware"
- "spacial consistency", "crossfade", "layout animation",

I made a motion vocabulary for this:
https://t.co/ExAxpr31no

[Lee Robinson @leerob]

Some tips to help agents understand your codebase:

1. The source code either needs to be the source of truth, or have something legible as a path to the source. For example, if marketing site content is actually stored in a CMS, you need to either delete the CMS and move that content into code, or make the CMS legible through and MCP, CLI, or skill: https://t.co/zhObygzELv

2. Agents need to be able to verify their work. This includes but is not limited to: using a typed language, having high-quality and fast tests, having a well-configured linter: https://t.co/AL3eY6TBXr

3. You need to have a concise and effective AGENTS.md file, which is included in every message to your agent. Models are quite good now, so some things you can omit as the models know them. You don’t need to say the tests live inside /tests for example. It’s worth asking the models to find things in your codebase and making sure they’re named what the models might expect, otherwise consider refactoring: https://t.co/2FlVQr84nO

4. Set up automations which give you suggestions for refactoring code, catching security issues which may have slipped through code review, and optionally continuous documentation of the codebase. You can effectively create a self-driving codebase which gets better while you sleep: https://t.co/UuYL3KNTZc

[Perplexity @perplexity_ai]

Introducing Search as Code, our new search architecture for AI agents.

It writes Python that calls our search stack directly, instead of looping through function calls one at a time.

Available in the Perplexity Agent API, and now default in Computer.

https://t.co/ut6GGWQTVO

[The Code Newsletter @code_newsletter]

🚨: Anthropic dropped new Claude Code best practices for Opus 4.7.

10 things every developer should know 👇

[The Code Newsletter @code_newsletter]

This 30-min workshop by the creator of Claude Code Boris Cherny will teach you more about vibe-coding than 100 YouTube video guides.

10 quick hands on insights👇

[The Code Newsletter @code_newsletter]

@karpathy shared how he actually uses LLMs day to day (10 points) 👇

He's spending most of his tokens building and maintaining a personal knowledge base on whatever he's actively researching.

Here's the full breakdown:

[The Code Newsletter @code_newsletter]

15 Claude Code Features that are under utilised

🧵:

[The Code Newsletter @CodeNewsletter]

This is what most engineers need!

[Zain Kahn @heykahn]

Stop hunting for tutorials and start building with this 50+ project roadmap.

Join 200K+ Engineers getting these resources for free: https://t.co/Bx5ml72xWm

It contains the best free courses, articles, tutorials, and videos to learn:

[The Code Newsletter @CodeNewsletter]

This prompt can single handedly manage Context for your Claude Code Project!

[Zain Kahn @heykahn]

Claude Code is only as good as your orchestration workflow.

I spent 6 months testing it so you don't have to.

[ P.S. You can get my Ultimate Claude Code guide for engineers here for free: https://t.co/Bx5ml72xWm ]

[The Code Newsletter @CodeNewsletter]

One of the most important reads of 2026

[Sebastian Raschka @rasbt]

https://t.co/7JkJxiUPv6

[The Code Newsletter @CodeNewsletter]

This feels so true - the job of a software engineer has shifted from coder to agent orchestrators or conductors!

[Andrej Karpathy @karpathy]

It is hard to communicate how much programming has changed due to AI in the last 2 months: not gradually and over time in the "progress as usual" way, but specifically this last December. There are a number of asterisks but imo coding agents basically didn’t work before December and basically work since - the models have significantly higher quality, long-term coherence and tenacity and they can power through large and long tasks, well past enough that it is extremely disruptive to the default programming workflow.

Just to give an example, over the weekend I was building a local video analysis dashboard for the cameras of my home so I wrote: “Here is the local IP and username/password of my DGX Spark. Log in, set up ssh keys, set up vLLM, download and bench Qwen3-VL, set up a server endpoint to inference videos, a basic web ui dashboard, test everything, set it up with systemd, record memory notes for yourself and write up a markdown report for me”. The agent went off for ~30 minutes, ran into multiple issues, researched solutions online, resolved them one by one, wrote the code, tested it, debugged it, set up the services, and came back with the report and it was just done. I didn’t touch anything. All of this could easily have been a weekend project just 3 months ago but today it’s something you kick off and forget about for 30 minutes.

As a result, programming is becoming unrecognizable. You’re not typing computer code into an editor like the way things were since computers were invented, that era is over. You're spinning up AI agents, giving them tasks *in English* and managing and reviewing their work in parallel. The biggest prize is in figuring out how you can keep ascending the layers of abstraction to set up long-running orchestrator Claws with all of the right tools, memory and instructions that productively manage multiple parallel Code instances for you. The leverage achievable via top tier "agentic engineering" feels very high right now.

It’s not perfect, it needs high-level direction, judgement, taste, oversight, iteration and hints and ideas. It works a lot better in some scenarios than others (e.g. especially for tasks that are well-specified and where you can verify/test functionality). The key is to build intuition to decompose the task just right to hand off the parts that work and help out around the edges. But imo, this is nowhere near "business as usual" time in software.

[The Code Newsletter @CodeNewsletter]

This is a solid roadmap for engineers to crack AI and ML Engineering

[Zain Kahn @heykahn]

You can now skip the guess work on how to master AI and Machine Learning.

After consulting with over 10 AI engineers, I've mapped out the definitive learning path:

1. Find a community to stay current on the latest tech.
- Access resources built by engineers: https://t.co/FkYGv48nkG

2. Build a solid foundation in mathematics.
- Master the essentials here: https://t.co/1xueysmc3N

This roadmap is the shortest distance between where you are and expertise.  https://t.co/y0rCcR1jUf

[The Code Newsletter @CodeNewsletter]

This repo single handedly can teach you more about Claude Code than 12 hour tutorials!

[Om Nalinde @that__aum]

I've technically lived inside Claude Code for last 6 months (This is where engineers should start)

"Everything Claude Code" repo has already bagged 50K stars and it is the one resource you need to start with Claude Code.

[ P.S. You can also get my Ultimate Claude Code guide for engineers here at no cost: https://t.co/daqcOmY4xF

Here's what it actually gives you:

→ 13+ specialized sub-agents for things you shouldn't be doing manually.

→ 48+ workflow "skills" covering TypeScript, Python, Go, Java, React/Next.js, API design, databases, and ClickHouse analytics.

→ 32+ slash commands for Feature planning, multi-agent orchestration, package manager setup.

→ Language-specific .clauderules so you only install what you need.

→ MCP configs and hooks that trigger on events like session start and end.

→ Two solid guides. A quick-start for getting going, and a deep dive.

It also has a continuous learning system that pulls patterns from your coding sessions and saves them as reusable "instincts." Your setup gets better the more you use it.

[Zain Kahn @heykahn]

Stop coding from scratch with agents.

This repo gives you 860+ battle-tested skills for Claude Code, Gemini CLI, Cursor, and Copilot.

It’s essentially a curated library that transforms your AI assistant from a basic chat interface into a production-ready engineering partner.

P.S. Check out 100+ such repos shared in this community of 200K+ AI/ML Engineers:
https://t.co/cSeCMfne9l

You can use it for:
→ RAG pipelines & LLM systems
→ Docker, AWS serverless, Vercel deployment
→ Security audits & vulnerability testing
→ Full-stack development patterns
→ TDD & QA automation
→ Growth, SEO, pricing strategy
…and much more!

Skills are grouped into role-based bundles (Web Dev, Security Engineer, DevOps, etc.) to help you get started quickly without manually exploring hundreds of skills.

Works across Claude Code, Cursor, Gemini CLI, Codex CLI, Antigravity IDE, GitHub Copilot, OpenCode, and AdaL CLI.

Install with one command:
npx antigravity-awesome-skills