Diego Dora (Mr. D) (@diegohdora) header
Diego Dora (Mr. D) (@diegohdora) profile picture

Founder @DIFFConsulting . Host @Cloudicorns 🦄 https://nitter.gq/t.co/toXrkL2vlo SAP Mentor @SAPMentors Try not. Do. Or do not. There is no try.

@Buenos Aires, Argentina
Joined January 2011
Photo by Diego Dora (Mr. D) (@diegohdora)Photo by Diego Dora (Mr. D) (@diegohdora)Photo by Diego Dora (Mr. D) (@diegohdora)Photo by Diego Dora (Mr. D) (@diegohdora)Photo by Diego Dora (Mr. D) (@diegohdora)Photo by Diego Dora (Mr. D) (@diegohdora)Photo by Diego Dora (Mr. D) (@diegohdora)
Enjoy this wonderful Documentary celebrating the 10 YEARS of the @SAPMentors Program. It's an honor be part of this WONDERFUL FAMILY and a HUGE responsibility to share my personal story here. Thank you for the opportunity! GRACIAS! nitter.gq/SAPMentors/status/9432…
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Well well well… 😂
Introducing Microsoft-Decision-1, our new model for fast decision-making. It delivers top performance on structured decision tasks, outperforming both LLMs and other decision models in latency and quality. We’re already testing it across Microsoft for everything from incident response and quality control to scientific discovery.
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Diego Dora (Mr. D) retweeted
Instead of watching 2 hours of Netflix tonight, watch this in-depth interview with Kimi founder Zhilin Yang. He explains why everyone’s racing on reasoning while Claude quietly bet on agents - but the real hard part isn’t the agent itself, it’s the model underneath. His memorable metaphor: a pure reasoning model is like “a brain in a fish tank” - it can think forever but never touches the world. An agent is that brain wired to tools, memory, and action. Yang shares how they’re thinking about K2 helping build K3 through strong agent skills, the biggest bottlenecks in generalization, test-time scaling in two dimensions, and even how RL-style thinking applies to both models and running teams. Whether you’re building agents, scaling models, or just want to understand frontier AI thinking beyond the usual Western narratives, this is one of the clearest and most thoughtful long-form discussions available right now. Bookmark it & watch - it might be the most valuable 100 minutes you spend on real AI engineering and strategy this month.
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Diego Dora (Mr. D) retweeted
🧑‍🏫3hr Long Deep Agents Course Great course from a member of the community on Deep Agents Covers task planning, file systems for context management, subagent-spawning, and long-term memory youtube.com/watch?v=J8DzuMmS…
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Diego Dora (Mr. D) retweeted
Launching the 𝙾𝚙𝚎𝚗 𝚃𝚊𝚐 repo. A better, open-source Claude Tag. With support for Generative UI, Human in the Loop, Streaming replies, and Full threads context. The same agent runs in MS Teams, Discord, Telegram, and WhatsApp. Get started in minutes: github.com/CopilotKit/OpenTa…
Introducing Open Tag. A better, open-source Claude Tag. Works with any model, any agent harness, and fully custom agents. Supports → Generative UI → Streaming replies → Human in the Loop approvals → Full thread context Slack and MS Teams today. Discord, Google Chat, WhatsApp soon. Request early access: go.copilotkit.ai/beyond-the-…
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Introducing Sakana Fugu: A full multi-agent orchestration system accessible via a single model API. Our ‘Fugu Ultra’ model matches the performance of Fable and Mythos, delivering frontier capability without the risk of export controls. Try it: sakana.ai/fugu 🐡
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"Build your own Claude Code with Deep Agents" Good article by the community showing how to build a Claude Code-like agent using Deep Agents Especially relevant given how strong GLM-5.2 seems to be! pub.towardsai.net/build-your…
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Diego Dora (Mr. D) retweeted
How do you support full-text search JSON filtering over agent traces that span up to hundreds of MBs, while keeping a median (P50) latency of 400ms? Here’s an inside look at how we built a custom inverted index from scratch for SmithDB. langchain.com/blog/full-text…
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Diego Dora (Mr. D) retweeted
Andrej Karpathy admits he’s struggling with AI
Replying to @karpathy
@karpathy and I are back! At @sequoia AI Ascent 2026. And a lot has changed. Last year, he coined “vibe coding”. This year, he’s never felt more behind as a programmer. The big shift: vibe coding raised the floor. Agentic engineering raises the ceiling. We talk about what it means to build seriously in the agent era. Not just moving faster. Building new things, with new tools, while preserving the parts that still require human taste, judgment, and understanding.
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Diego Dora (Mr. D) retweeted
The Google Threat Intelligence Group has detected the first known instance of a threat actor using an AI-developed zero-day exploit in the wild. While the attackers planned a wide-scale strike, our proactive counter-discovery may have prevented that from happening. This finding is part of our new report on AI-powered threats.
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Diego Dora (Mr. D) retweeted
This works really well btw, at the end of your query ask your LLM to "structure your response as HTML", then view the generated file in your browser. I've also had some success asking the LLM to present its output as slideshows, etc. More generally, imo audio is the human-preferred input to AIs but vision (images/animations/video) is the preferred output from them. Around a ~third of our brains are a massively parallel processor dedicated to vision, it is the 10-lane superhighway of information into brain. As AI improves, I think we'll see a progression that takes advantage: 1) raw text (hard/effortful to read) 2) markdown (bold, italic, headings, tables, a bit easier on the eyes) <-- current default 3) HTML (still procedural with underlying code, but a lot more flexibility on the graphics, layout, even interactivity) <-- early but forming new good default ...4,5,6,... n) interactive neural videos/simulations Imo the extrapolation (though the technology doesn't exist just yet) ends in some kind of interactive videos generated directly by a diffusion neural net. Many open questions as to how exact/procedural "Software 1.0" artifacts (e.g. interactive simulations) may be woven together with neural artifacts (diffusion grids), but generally something in the direction of the recently viral nitter.gq/zan2434/status/2046982… There are also improvements necessary and pending at the input. Audio nor text nor video alone are not enough, e.g. I feel a need to point/gesture to things on the screen, similar to all the things you would do with a person physically next to you and your computer screen. TLDR The input/output mind meld between humans and AIs is ongoing and there is a lot of work to do and significant progress to be made, way before jumping all the way into neuralink-esque BCIs and all that. For what's worth exploring at the current stage, hot tip try ask for HTML.
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Diego Dora (Mr. D) retweeted
Farzapedia, personal wikipedia of Farza, good example following my Wiki LLM tweet. I really like this approach to personalization in a number of ways, compared to "status quo" of an AI that allegedly gets better the more you use it or something: 1. Explicit. The memory artifact is explicit and navigable (the wiki), you can see exactly what the AI does and does not know and you can inspect and manage this artifact, even if you don't do the direct text writing (the LLM does). The knowledge of you is not implicit and unknown, it's explicit and viewable. 2. Yours. Your data is yours, on your local computer, it's not in some particular AI provider's system without the ability to extract it. You're in control of your information. 3. File over app. The memory here is a simple collection of files in universal formats (images, markdown). This means the data is interoperable: you can use a very large collection of tools/CLIs or whatever you want over this information because it's just files. The agents can apply the entire Unix toolkit over them. They can natively read and understand them. Any kind of data can be imported into files as input, and any kind of interface can be used to view them as the output. E.g. you can use Obsidian to view them or vibe code something of your own. Search "File over app" for an article on this philosophy. 4. BYOAI. You can use whatever AI you want to "plug into" this information - Claude, Codex, OpenCode, whatever. You can even think about taking an open source AI and finetuning it on your wiki - in principle, this AI could "know" you in its weights, not just attend over your data. So this approach to personalization puts *you* in full control. The data is yours. In Universal formats. Explicit and inspectable. Use whatever AI you want over it, keep the AI companies on their toes! :) Certainly this is not the simplest way to get an AI to know you - it does require you to manage file directories and so on, but agents also make it quite simple and they can help you a lot. I imagine a number of products might come out to make this all easier, but imo "agent proficiency" is a CORE SKILL of the 21st century. These are extremely powerful tools - they speak English and they do all the computer stuff for you. Try this opportunity to play with one.
This is Farzapedia. I had an LLM take 2,500 entries from my diary, Apple Notes, and some iMessage convos to create a personal Wikipedia for me. It made 400 detailed articles for my friends, my startups, research areas, and even my favorite animes and their impact on me complete with backlinks. But, this Wiki was not built for me! I built it for my agent! The structure of the wiki files and how it's all backlinked is very easily crawlable by any agent + makes it a truly useful knowledge base. I can spin up Claude Code on the wiki and starting at index.md (a catalog of all my articles) the agent does a really good job at drilling into the specific pages on my wiki it needs context on when I have a query. For example, when trying to cook up a new landing page I may ask: "I'm trying to design this landing page for a new idea I have. Please look into the images and films that inspired me recently and give me ideas for new copy and aesthetics". In my diary I kept track of everything from: learnings, people, inspo, interesting links, images. So the agent reads my wiki and pulls up my "Philosophy" articles from notes on a Studio Ghibli documentary, "Competitor" articles with YC companies whose landing pages I screenshotted, and pics of 1970s Beatles merch I saved years ago. And it delivers a great answer. I built a similar system to this a year ago with RAG but it was ass. A knowledge base that lets an agent find what it needs via a file system it actually understands just works better. The most magical thing now is as I add new things to my wiki (articles, images of inspo, meeting notes) the system will likely update 2-3 different articles where it feels that context belongs, or, just creates a new article. It's like this super genius librarian for your brain that's always filing stuff for your perfectly and also let's you easily query the knowledge for tasks useful to you (ex. design, product, writing, etc) and it never gets tired. I might spend next week productizing this, if that's of interest to you DM me + tell me your usecase!
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Diego Dora (Mr. D) retweeted
Congrats to the @cursor_ai team on the launch of Composer 2! We are proud to see Kimi-k2.5 provide the foundation. Seeing our model integrated effectively through Cursor's continued pretraining & high-compute RL training is the open model ecosystem we love to support. Note: Cursor accesses Kimi-k2.5 via @FireworksAI_HQ ' hosted RL and inference platform as part of an authorized commercial partnership.
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Diego Dora (Mr. D) retweeted
🚨 BREAKING: Someone just open-sourced a full offline survival computer with AI, Wikipedia, and maps built in. Project N.O.M.A.D. is an open-source offline survival computer. Self-contained. Zero internet required after install. Zero telemetry. Everything runs locally on your hardware. What it includes: → Full Wikipedia archives via Kiwix → Offline maps via OpenStreetMap → Local AI models via Ollama + Open WebUI → Calculators, reference tools, resource libraries → A management UI to control everything from a browser One curl command installs the entire system on any Debian-based machine. Runs headless as a server so any device on your local network can access it. Minimum specs to run the base system: dual-core processor, 4GB RAM, 5GB storage. To run local LLMs offline, you want 32GB RAM and an NVIDIA RTX 3060 or better. No accounts. No authentication by default. No cloud dependency. No phone-home behavior. Built to function when nothing else does. The grid, the cloud, the API you depend on. None of it is guaranteed. The people building local-first systems right now are the ones who won’t be asking for help when access disappears.
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Diego Dora (Mr. D) retweeted
In 2018, Stanford professor Matt Abrahams gave a masterclass on why most people fail to communicate well. He broke down: - The structure every message needs - Why audiences stop listening - The psychology of attention 15 lessons that'll make your communication unforgettable:
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Diego Dora (Mr. D) retweeted
Pay attention to this one if you are building terminal-based coding agents. OpenDev is an 81-page paper covering scaffolding, harness design, context engineering, and hard-won lessons from building CLI coding agents. It introduces a compound AI system architecture with workload-specialized model routing, a dual-agent architecture separating planning from execution, lazy tool discovery, and adaptive context compaction. The industry is shifting from IDE plugins to terminal-native agents. Claude Code, Codex CLI, and others have proven the model works. This paper formalizes the design patterns that make these systems reliable, covering topics like event-driven system reminders to counteract instruction fade-out, automated memory across sessions, and strict safety controls for autonomous operation. Paper: arxiv.org/abs/2603.05344 Learn to build effective AI agents in our academy: academy.dair.ai/
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Diego Dora (Mr. D) retweeted
"You wouldn't ship code without tests, but why ship skills without evals? This is a practical guide to fixing that." philschmid.de/testing-skills < @_philschmid making us smarter on defining success for Skills and iterating on evaluations.
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everyone is building skills, but how do you know if they help? we wrote up our experience of trying to answer this question
📊 How to evaluate skills❓️ Lots of companies are building skills for coding agents. But how do you know if your skill is actually working? It's tempting to go by vibes, but performance varies a lot across tasks — and coding agents have a huge action space, which makes that variance even harder to predict. We built an evaluation benchmark for our newly released LangSmith and LangChain skills. ➡️ Learn about our findings here: blog.langchain.com/evaluatin… ➡️ Check out the benchmark for yourself: github.com/langchain-ai/skil…
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Diego Dora (Mr. D) retweeted
Frameworks for training AI agents in product management github.com/deanpeters/Produc…
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