Harsh @Har_ssh7

Very rare to see stars in Bangalore

@paraschopra fantasy world game where AI villagers live real lives, they have there own brain, they gossip, they show evolution can occur among ai npcs

@UnrealEngine Was waiting for this

@joon_s_pk Your feedback would be really helpful.

Claude Fable one-shotted this entire 3D game based on Stanford's Generative Agents paper. My prompt: "build me a fantasy world game where AI villagers live real lives — read the papers, do whatever it takes." 9 villagers with memories, daily plans & gossip. Emerging AI Agents.

Claude @claudeai

3 weeks ago

Introducing Claude Fable 5: a Mythos-class model that we’ve made safe for general use. Its capabilities exceed those of any model we’ve ever made generally available.

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@mattshumer_ insane , im also building really fun , will be making it public tonight excited

Bangerr

Dan Shipper 📧 @danshipper

3 weeks ago

BREAKING: Anthropic just dropped Claude Fable 5—this is Mythos, made safe for public release. It is the best coding model in the world. We've been testing it internally @every for the last week or so across coding, writing, marketing, editing, and more—here's our vibe check: -

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One of the new, buzzy jobs in Silicon Valley is the AI Forward Deployed Engineer (FDE), an engineer who is embedded within a client organization to help customize solutions, such as building and tuning agentic workflows that suit the client’s particular needs. I’ve heard from people who are wondering anew about the FDE career path since OpenAI and Anthropic started building new teams to place FDEs within client organizations. The rise of FDEs for AI workloads is one way AI is creating new jobs (and why the jobpolcalypse narrative of upcoming job market collapse is false -- there will be many AI and non-AI jobs). However, I believe there will be far more AI Engineer jobs than FDEs, as I explain below. The FDE role was pioneered about two decades ago by Palantir, which sent engineers to government locations to work on secure, air-gapped networks. In addition to having good technical skills, FDEs need communication skills and sometimes business skills. For example, they may need to speak with clients to understand their needs, formulate a strategy to prioritize projects, explain complex technology, and respectfully push back if a client asks for something unrealistic. They’re enjoying a resurgence because of the amount of work involved in taking an off-the-shelf LLM and building it into a custom agentic workflow that fits particular business needs. However, I believe the number of AI Engineer jobs will be far larger. A company might accept a few FDEs to be embedded within its organization. But most companies will want far more of their own employees working on their projects. While my organizations do hire FDEs, we hire far more AI Engineers! Also, a common client concern is that it is hard to find vendor-neutral FDEs — they are, after all, there to deeply integrate a particular vendor’s product into a company. In this moment when it’s hard to predict which AI service will be the best one in a year’s time, optionality (the ability to pick whatever vendor turns out to fit best in the future) is very valuable. In contrast, letting FDEs tightly bind a company’s processes significantly reduces optionality. Right now, I see surging demand for AI Engineers who can build software applications using AI software components (like LLM prompting, agentic frameworks, evals, etc.) and effectively use AI coding agents (like Claude Code, Codex, Antigravity CLI, and OpenCode). As the AI Engineer role matures, I expect it to fragment into more specialized roles, like the generic Software Engineer role from decades ago fragmented into frontend, backend, mobile, data engineering, devops, and so on. What will be the future, specialized AI engineering roles? I don’t know. Perhaps there will be AI FDEs, LLMOps Engineers, Evals Engineers, AI Data Engineers, Harness Engineers, and other roles we don’t have names for yet. But for now, I see a lot of AI engineers who are generalists create a lot of value. Skilled AI Engineers are in very high demand! As our field continues to mature over the coming decade, I look forward to new specializations within AI Engineering that create even more job opportunities. [Original text: The Batch newsletter]

Guys you won't regret checking this out

Ishant @koharishant

a month ago

We just went live on Product Hunt. CASE is our desktop automation API for macOS. It lets agents (Claude Code, Cursor, your own agents) actually drive DaVinci Resolve and any other Mac app from a few lines of code. Months of bootstrapping into this one, and today it goes out

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Ads, Marketplace and IP That's all it takes

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Failure isn’t the opposite of success — it’s part of it. Every mistake teaches. Every setback builds you. Stop fearing failure. Start using it. 🔥

You gotta choose

@koharishant Aliens

🛑Stanford CME295 might be the best free resource to actually understand Transformers & LLMs. Bookmark this. Give it 2 hours today. It might be the highest ROI thing you do this month: #AI #LLM #agents

X is drowning in AI buzzwords Prompt eng → Context eng → Harness Same pattern new label Tools change, but the real moat is: → compounding intelligence → building self-improving systems @karpathy what’s the next overhyped but directionally correct idea?

tldr > evals are the new training data. instead of updating weights, you're updating the agent harness > problem is agents are famous cheaters. they will reward-hack your evals and overfit just to make the score go up > solution is treat evals like real ml. you need strict train/test splits and holdout sets to force actual generalization > quality over quantity. a few hand-curated, highly-tagged evals easily beat thousands of noisy ones > the better-harness loop is mine prod traces for failures -> turn into evals -> auto-tweak prompts & tools -> validate > the ultimate flywheel is more prod usage -> more failure traces -> more evals -> continuously self-improving agents

Viv @Vtrivedy10

3 months ago

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Harness, Memory, Context Fragments, & the Bitter Lesson this is a work in progress mental dump on interesting intersections between how we use and design a harness, implications for memory being accumulated over long timescales, and the search bitter lesson we can’t escape this is v30+, HTML diagrams help me iteratively refine + chat to roughly “see” and alter the mental model Harnesses & Context Fragments: a very important job of the harness is to efficiently & correctly route data within its boundaries into the context window boundary for computation to happen the context window is a precious artifact. Harnesses make decisions on how to populate, manage, edit, and organize it so agents can do work. Each loaded object can be thought of as a Context Fragment and represents an explicit decision by the user and harness designer of what needs a model needs to do work at any given time. many ideas on externalizing objects + loading into the context window are pioneered and very well described by @a1zhang with RLMs Experiential Memory: we’re in the very early days of deploying agents and agents produce massive amounts of data in every interaction they have. this is akin to humans doing things and remembering things they did. however agent memory has a massive advantage as it can be accumulated across all agents which are easily forked and duplicated (unlike humans). @dwarkesh_sp does a good talking about this massive benefit of artificial systems memory can be treated as an externalized object. the harness is tasked with doing good contextualized retrieval which means pulling in the right data from accumulated memories across all agent interactions Search & The Bitter Lesson: As we deploy agents in our world over year timescales, there is going to be a hyper-exponential in the amount of data produced by those agents. We should want to: 1. Own that data for ourselves. Open ecosystems are important here 2. Use that data This means that we’ll have to search over, distill, and organize massive amounts of data. Our brain is exceptional at doing this. Both contextually using prior experience and mostly committing the right stuff to memory with enough intentional practice. Our current infrastructure systems and algorithms will be put to the test and often break as we get used to this new data regime some open questions: - how do we efficiently distill experiences (Traces) into higher level memory primitives that capture the important parts? How do we do this over ultra long time horizons? - How much of the future is Search just-in-time vs Search that gets integrated into model weights? - How do we make models much better at self-managing their context window? How do we reduce error rates in recursively allowing agents to operate over external objects? i’ll be expanding on, altering, and adjusting these mental models but these feel like an important subset to me on the future of designing agents practically

The world is transitioning to a compute-powered economy. The field of software engineering is currently undergoing a renaissance, with AI having dramatically sped up software engineering even over just the past six months. AI is now on track to bring this same transformation to every other kind of work that people do with a computer. Using a computer has always been about contorting yourself to the machine. You take a goal and break it down into smaller goals. You translate intent into instructions. We are moving into a world where you no longer have to micromanage the computer. More and more, it adapts to what you want. Rather doing work with a computer, the computer does work for you. The rate, scale, and sophistication of problem solving it will do for you will be bound by the amount of compute you have access to. Friction is starting to disappear. You can try ideas faster. You can build things you would not have attempted before. Small teams can do what used to require much larger ones, and larger ones may be capable of unprecedented feats. More and more, people can turn intent into software, spreadsheets, presentations, workflows, science, and companies. People are spending less energy managing the tool and more energy focusing on what they are actually trying to create. That shift brings a kind of joy back into work that many people haven’t felt in a long time. Everyone can just build things with these tools. This is disruptive. Institutions will change, and the paths and jobs that people assumed were stable may not hold. We don’t know exactly how it will play out and we need to take mitigating downsides very seriously, as well as figuring out how to support each other as a society and world through this time. But there is something very freeing about this moment. For the first time, far more people can become who they want to become, with fewer barriers between an idea and a reality. OpenAI’s mission implies making sure that, as the tools do more, humans are the ones who set their intent and that the benefits are broadly distributed, rather than empowering just one or a small set of people. We're already seeing this in practice with ChatGPT and Codex. Nearly a billion people are using these systems every week in their personal and work lives. Token usage is growing quickly on many use-cases, as the surface of ways people are getting value from these models keeps expanding. Ten years ago, when we started OpenAI, we thought this moment might be possible. It’s happening on the earlier side, and happening in a much more interesting and empowering way for everyone than we’d anticipated (for example, we are seeing an emerging wave of entrepreneurship that we hadn’t previously been anticipating). And at the same time, we are still so early, and there is so much for everyone to define about how these systems get deployed and used in the world. The next phase will be defined by systems that can do more — reason better, use tools better, plan over longer horizons, and take more useful actions on your behalf. And there are horizons beyond, as AI starts to accelerate science and technology development, which have the potential to truly lift up quality of life for everyone. All of this is starting to happen, in small ways and large, today, and everyone can participate. I feel this shift in my own work every day, and see a roadmap to much more useful and beneficial systems. These systems can truly benefit all of humanity.