Elso Labs - AI Product & Source Intelligence

- create new software is easier now. what is not easy to envisage a clear product north star #createingai #aicreations

The cybersecurity sector sold off sharply following the announcement. CrowdStrike fell 7%, Palo Alto Networks dropped 6%, Zscaler declined 4.5%, and Okta, SentinelOne, and Fortinet each fell around 3%. The iShares Expanded Tech-Software Sector ETF dropped 3.7%, with Palantir, Microsoft, Oracle, Salesforce, and Palo Alto Networks all lower. The cumulative market value destruction across the broader IT sector has been reported at approximately $2 trillion, newnex.io/blogs/artifici… @NewnexHQ @SeinetHQ @ElsoLabs #Claude #AI

We're introducing Cursor Automations to build always-on agents.

Introducing Claude Code Security, now in limited research preview. It scans codebases for vulnerabilities and suggests targeted software patches for human review, allowing teams to find and fix issues that traditional tools often miss. Learn more: anthropic.com/news/claude-co…

Bought a new Mac mini to properly tinker with claws over the weekend. The apple store person told me they are selling like hotcakes and everyone is confused :) I'm definitely a bit sus'd to run OpenClaw specifically - giving my private data/keys to 400K lines of vibe coded monster that is being actively attacked at scale is not very appealing at all. Already seeing reports of exposed instances, RCE vulnerabilities, supply chain poisoning, malicious or compromised skills in the registry, it feels like a complete wild west and a security nightmare. But I do love the concept and I think that just like LLM agents were a new layer on top of LLMs, Claws are now a new layer on top of LLM agents, taking the orchestration, scheduling, context, tool calls and a kind of persistence to a next level. Looking around, and given that the high level idea is clear, there are a lot of smaller Claws starting to pop out. For example, on a quick skim NanoClaw looks really interesting in that the core engine is ~4000 lines of code (fits into both my head and that of AI agents, so it feels manageable, auditable, flexible, etc.) and runs everything in containers by default. I also love their approach to configurability - it's not done via config files it's done via skills! For example, /add-telegram instructs your AI agent how to modify the actual code to integrate Telegram. I haven't come across this yet and it slightly blew my mind earlier today as a new, AI-enabled approach to preventing config mess and if-then-else monsters. Basically - the implied new meta is to write the most maximally forkable repo and then have skills that fork it into any desired more exotic configuration. Very cool. Anyway there are many others - e.g. nanobot, zeroclaw, ironclaw, picoclaw (lol @ prefixes). There are also cloud-hosted alternatives but tbh I don't love these because it feels much harder to tinker with. In particular, local setup allows easy connection to home automation gadgets on the local network. And I don't know, there is something aesthetically pleasing about there being a physical device 'possessed' by a little ghost of a personal digital house elf. Not 100% sure what my setup ends up looking like just yet but Claws are an awesome, exciting new layer of the AI stack.

10 GitHub repositories that will teach you more practical AI engineering than most paid courses: 1. AI Agents for Beginners (Microsoft) github.com/microsoft/ai-a… 2. Awesome Generative AI Guide github.com/aishwaryanr/aw… 3. Designing Machine Learning Systems (Resources) github.com/chiphuyen/dmls… 4. GenAI Agents github.com/NirDiamant/Gen… 5. Hands-On AI Engineering github.com/Sumanth077/Han… 6. Hands-On Large Language Models github.com/HandsOnLLM/Han… 7. LLM Course github.com/mlabonne/llm-c… 8. Machine Learning for Beginners (Microsoft) github.com/microsoft/ML-F… 9. Made With ML github.com/GokuMohandas/M… 10. Prompt Engineering Guide github.com/dair-ai/Prompt…

Yesterday, we released Sarvam 30B and Sarvam 105B. Built from scratch, both models leverage a Mixture of Experts (MoE) architecture, delivering stronger performance at scale while using compute more efficiently.

NO WAYYY Claude in PowerPoint is absolutely INSANE ! It’s so over…

3 Reasons people are buying M4 Mac mini for Clawdbot. 1. It has access to the Apple ecosystem like messages, calendar, reminders, etc. 2. You get a VERY capable and reliable mini PC for all things compute. 3. It’s just SO much cooler than a Raspberry Pi. PS it’s $499 on Amazon

Fun fact: OpenAI handles 800 million users on ChatGPT with just one PostgreSQL primary and 50 read replicas 🤯 Today, OpenAI published an engineering blog explaining how they scaled their Postgres setup to support a massive 800 million users using a single primary and 50 multi-region replicas. They dive into details around their scaling approach, the PgBouncer proxy, cache locking, and cascading read replicas. It is genuinely neat and impressive. I just published a video on my YouTube channel where I dissect the blog and break down the nuances. Give it a watch - it is short and fun.

GPU, TPU, Google, OpenAI, Nvidia @GavinSBaker youtube.com/watch?v=cmUo48… #ai #tech

AI’s Exponential Moment: Dario Amodei on Economic Shock, Safety, and What Comes Next open.substack.com/pub/elsoai/p/a… #ai @DarioAmodei #anthropic @wef #tech

Energy-based models (EBMs) conceptualize learning as shaping a potential field where valid interpretations of the world settle into low-energy valleys and contradictions are repelled. saanyaojha.substack.com/p/the-man-who-… @ylecun #ai #LLMs

follow on wahtsapp channel whatsapp.com/channel/0029Vb…

“Workslop” describes AI‑generated output that looks polished but lacks substance ... read more elsoai.substack.com/p/what-is-work…

ai intern interview question

AI coding assistants and IDEs list open.substack.com/pub/elsoai/p/a… #AI #Coding #IDE #Assistants

Kombai is an AI agent for frontend developers that turns Figma, images, and text prompts into production-ready UI code elsoai.substack.com/p/kombai-is-an… @Kombaico

"AI isn't replacing radiologists" good article Expectation: rapid progress in image recognition AI will delete radiology jobs (e.g. as famously predicted by Geoff Hinton now almost a decade ago). Reality: radiology is doing great and is growing. There are a lot of imo naive predictions out there on the imminent impact of AI on the job market. E.g. a ~year ago, I was asked by someone who should know better if I think there will be any software engineers still today. (Spoiler: I think we're going to make it). This is happening too broadly. The post goes into detail on why it's not that simple, using the example of radiology: - the benchmarks are nowhere near broad enough to reflect actual, real scenarios. - the job is a lot more multifaceted than just image recognition. - deployment realities: regulatory, insurance and liability, diffusion and institutional inertia. - Jevons paradox: if radiologists are sped up via AI as a tool, a lot more demand shows up. I will say that radiology was imo not among the best examples to pick on in 2016 - it's too multi-faceted, too high risk, too regulated. When looking for jobs that will change a lot due to AI on shorter time scales, I'd look in other places - jobs that look like repetition of one rote task, each task being relatively independent, closed (not requiring too much context), short (in time), forgiving (the cost of mistake is low), and of course automatable giving current (and digital) capability. Even then, I'd expect to see AI adopted as a tool at first, where jobs change and refactor (e.g. more monitoring or supervising than manual doing, etc). Maybe coming up, we'll find better and broader set of examples of how this is all playing out across the industry. About 6 months ago, I was also asked to vote if we will have less or more software engineers in 5 years. Exercise left for the reader. Full post (the whole The Works in Progress Newsletter is quite good): worksinprogress.news/p/why-ai-isnt-…