Kees van den Berg

I'm a pragmatic purist, technologist, father of Donnée and Dion with a enthusiast, creative and mad twist.

[Kees van den Berg @kees_v_d_berg]

@everestchris6 system

[David Sacks @DavidSacks]

I’ve had a number of conversations with folks inside and outside government about the current situation with Anthropic, and here is what I believe to be true:

— As we know, Anthropic publicly released its Mythos class models earlier this week under the commercial name Fable.

— Fable is Mythos with guardrails. But if those guardrails fail, then you’ve exposed Mythos and its advanced cyber capabilities to people who shouldn’t have them. (Keep in mind that Anthropic itself widely promoted the idea that Mythos was a cyberweapon and needed to be regulated as such. They asked for government regulation of Mythos and championed the guardrails on Fable. If there is a vulnerability — big or small — it is Anthropic’s responsibility to patch.)

— A highly credible trusted partner of both Anthropic and the USG who was testing Fable came forward with a jailbreak of those guardrails. The Admin asked Dario to fix the jailbreak or de-deploy the model. Dario refused.

— In their blog post, Anthropic defended its decision by saying the jailbreak isn’t serious. That is not what the trusted partner and the USG believe; nor is that kind of minimizing language consistent with Anthropic’s brand as the AI safety company. It’s difficult to fathom how they could claim a jailbreak allowing operability of a cyber weapon could be defined as not “serious.”

— In the past, Anthropic has always said that safety must be top priority and taken super seriously. In this case, Anthropic prioritized the continued offering of the consumer model over safety.

— In reaction, the Admin issued the export control. The Admin did this reluctantly. It’s been very surprised that Anthropic hasn’t wanted to cooperate with a reasonable safety request (ie fixing the jailbreak issue). Anthropic’s reaction is very much at odds with their branding and ethos as a safe AI research community.

— The Admin’s hope now is that Anthropic remediates the safety issue, the export control is lifted, and Fable goes back into general release. The Admin wants all of this to happen as soon as possible. It is frankly bewildered that Anthropic hasn’t wanted to comply with safety requests that it previously said were its highest priority.

— Those trying to misdirect and tie this action to the prior DoW/Anthropic issues are wrong. The Admin values Anthropic’s technical capabilities and feels that this issue, while serious, should be easily resolved. The ball is in Anthropic’s court.

[Chaz Wargnier ♨️ @DataChaz]

🚨 You need to see this.

@addyosmani from Google just dropped his new Agent Skills and it's incredible.

It brings 19 engineering skills + 7 commands to AI coding agents, all inspired by Google best practices 🤯

AI coding agents are powerful, but left alone, they take shortcuts.

They skip specs, tests, and security reviews, optimizing for "done" over "correct." Addy built this to fix that.

Each skill encodes the workflows and quality gates that senior engineers actually use: spec before code, test before merge, measure before optimize.

The full lifecycle is covered:

→ Define - refine ideas, write specs before a single line of code
→ Plan - decompose into small, verifiable tasks
→ Build - incremental implementation, context engineering, clean API design
→ Verify - TDD, browser testing with DevTools, systematic debugging
→ Review - code quality, security hardening, performance optimization
→ Ship - git workflow, CI/CD, ADRs, pre-launch checklists

Features 7 slash commands: (/spec, /plan, /build, /test, /review, /code-simplify, /ship) that map to this lifecycle.

It works with:
✦ Claude Code
✦ Cursor
✦ Antigravity
✦ ... and any agent accepting Markdown. Baking in Google-tier engineering culture (Shift Left, Chesterton's Fence, Hyrum's Law) directly into your agent's step-by-step workflow!

`npx skills add addyosmani/agent-skills`

Free and open-source.

Repo link in 🧵↓

[Andrej Karpathy @karpathy]

LLM Knowledge Bases

Something I'm finding very useful recently: using LLMs to build personal knowledge bases for various topics of research interest. In this way, a large fraction of my recent token throughput is going less into manipulating code, and more into manipulating knowledge (stored as markdown and images). The latest LLMs are quite good at it. So:

Data ingest:
I index source documents (articles, papers, repos, datasets, images, etc.) into a raw/ directory, then I use an LLM to incrementally "compile" a wiki, which is just a collection of .md files in a directory structure. The wiki includes summaries of all the data in raw/, backlinks, and then it categorizes data into concepts, writes articles for them, and links them all. To convert web articles into .md files I like to use the Obsidian Web Clipper extension, and then I also use a hotkey to download all the related images to local so that my LLM can easily reference them.

IDE:
I use Obsidian as the IDE "frontend" where I can view the raw data, the the compiled wiki, and the derived visualizations. Important to note that the LLM writes and maintains all of the data of the wiki, I rarely touch it directly. I've played with a few Obsidian plugins to render and view data in other ways (e.g. Marp for slides).

Q&A:
Where things get interesting is that once your wiki is big enough (e.g. mine on some recent research is ~100 articles and ~400K words), you can ask your LLM agent all kinds of complex questions against the wiki, and it will go off, research the answers, etc. I thought I had to reach for fancy RAG, but the LLM has been pretty good about auto-maintaining index files and brief summaries of all the documents and it reads all the important related data fairly easily at this ~small scale.

Output:
Instead of getting answers in text/terminal, I like to have it render markdown files for me, or slide shows (Marp format), or matplotlib images, all of which I then view again in Obsidian. You can imagine many other visual output formats depending on the query. Often, I end up "filing" the outputs back into the wiki to enhance it for further queries. So my own explorations and queries always "add up" in the knowledge base.

Linting:
I've run some LLM "health checks" over the wiki to e.g. find inconsistent data, impute missing data (with web searchers), find interesting connections for new article candidates, etc., to incrementally clean up the wiki and enhance its overall data integrity. The LLMs are quite good at suggesting further questions to ask and look into.

Extra tools:
I find myself developing additional tools to process the data, e.g. I vibe coded a small and naive search engine over the wiki, which I both use directly (in a web ui), but more often I want to hand it off to an LLM via CLI as a tool for larger queries.

Further explorations:
As the repo grows, the natural desire is to also think about synthetic data generation + finetuning to have your LLM "know" the data in its weights instead of just context windows.

TLDR: raw data from a given number of sources is collected, then compiled by an LLM into a .md wiki, then operated on by various CLIs by the LLM to do Q&A and to incrementally enhance the wiki, and all of it viewable in Obsidian. You rarely ever write or edit the wiki manually, it's the domain of the LLM. I think there is room here for an incredible new product instead of a hacky collection of scripts.

[Kees van den Berg @kees_v_d_berg]

@TechWith_Nova banana

[Elon Musk @elonmusk]

Wisdom

[Kees van den Berg @kees_v_d_berg]

@boztank @afoxdesign Would be great, will this than come to the Quest 3?

[EventModeling @EventModeling]

There's no better way to move projects forward than with true collaboration.

[Per Ökvist @per_okvist]

One of the highlights of the day so far, at @EventSrcLive - @thinkb4coding live coding with deciders showing their impact on aggregate and process manager design.
#dddeu
@tretton37ab

[Kees van den Berg @kees_v_d_berg]

Start of #eventsourcing conference @ddd_eu ,
3 days to get inspired

[Kees van den Berg @kees_v_d_berg]

@SuxoiKorm did you use anything special for a wearos flutterapp

[Vaughn Vernon @VaughnVernon]

#DDDesign thinking from "Implementing Domain-Driven Design" Ch2:

"Supporting or Generic doesn’t mean unimportant... yet there is no need for the business to excel in these areas. It’s the Core Domain that requires excellence... since it will provide distinct advantages..."

[Kees van den Berg @kees_v_d_berg]

@HeidyVarkeviss2 @irPaulusJansen Mijn excuus, hij is verwijderd

[Formula 1 @F1]

That extraordinary final lap of the 2021 title race in full 😮

#AbuDhabiGP 🇦🇪 #F1

[Dmitry Jemerov @intelliyole]

The most highly voted feature in the JetBrains issue tracker - collaborative editing support - finally has an implementation, released as a limited Early Access Preview today. See https://t.co/bYOKO1cpHB for instructions to try it out.

[Lars Boelen @LarsBoelen]

Een HALF MILJOEN voor een waterstof oplaad station voor fietsen. De decennia oude elektrische fiets kan toe met een stopcontact a €6,64 maar er is altijd wel een gemeente te vinden die wil laten zien dat ze niets begrijpt van waar we staan en waar het naar toe gaat. 🥳

[Karine Jean-Pierre @K_JeanPierre]

Present Obama warned us 👇🏿

[Kees van den Berg @kees_v_d_berg]

If u fly with #tui take a trolley allong instead of a backpack U get preferance above backpacks in the cabinets because ??? #trolleyterror 😭