Ryan Mitchell

"Graphs make provenance first-class. Instead of burying "where did this come from?" inside a dense vector, a graph records sources, activities, and agents as nodes and edges you can query..." https://lnkd.in/efAdV9dx

26

This Karpathy guy is _extremely_ credible, so when he says "AI agents are slop", it maybe means something. There's actually a lot more fascinating detail about gradient descent and log probs and LLM-as-judge in this podcast, definitely worth listening to if you're into the technical nuance, but let's stay at the high level here. The interesting part about this podcast is that it's really balanced. It's not "AGI is around the corner" and it's not "AI is just a bubble". His view is a balanced middle view that AI will change a lot of things but gradually, and despite the research progress so far there's a huge amount still to come that isn't easy at all. It's worth watching or reading if you're trying to understand and predict the next 5-10 years (and right now who isn't!), because Karpathy has forgotten more about AI than most of us will ever know. Here's some excerpts from what he said: - "there's some over-prediction going on in the industry" - "You can’t just tell them something and they’ll remember it. They’re cognitively lacking and it’s just not working." - "I feel like the problems are tractable, they’re surmountable, but they’re still difficult." - "The models have so many cognitive deficits." - "It’s a total mess. It’s just not net useful. I can go in, I can clean it up, but it’s not net useful." - "They’re not very good at code that has never been written before" - "You’re not getting the richness and the diversity and the entropy from these models as you would get from humans." - "Look at the average example in the training set: factual mistakes, errors, nonsensical things." - "So this thing which is “general”, which should be able to do any knowledge work, is just overwhelmingly doing only coding." - "This assumption of suddenly having a completely intelligent, fully flexible, fully general human in a box, and we can dispense it at arbitrary problems in society, I don’t think that we will have this discrete change." - "For some kinds of tasks and jobs and so on, there’s a very large demo-to-product gap where the demo is very easy, but the product is very hard." - "I’m only sounding pessimistic because when I go on my Twitter timeline, I see all this stuff that makes no sense to me." But also: - "I see AI as fundamentally an extension of computing in a pretty fundamental way" - "Everything is gradually being automated and has been for hundreds of years." - "They’re going to get better, and it’s going to be wonderful." - "This is a miraculous technology that didn’t exist. There’s going to be a huge amount of demand, as we see the demand in ChatGPT already and so on."

95

49 Comments

Most people mix these two up. AI Agents and Agentic AI are not the same. This comparison shows where the difference really lies. 1. Autonomy - AI Agent: Works with limited freedom. It waits for inputs and follows defined rules. - Agentic AI: Operates on its own. It decides when and how to act based on the situation. 2. Goal handling - AI Agent: Completes a specific task exactly as instructed. - Agentic AI: Works toward a goal and decides the steps needed to reach it. 3. Learning behavior - AI Agent: Learns only within fixed boundaries or does not learn at all. - Agentic AI: Improves over time using feedback and past context. 4. Complexity handling - AI Agent: Performs well in simple, controlled environments. - Agentic AI: Handles dynamic systems with many moving parts. 5. Decision process - AI Agent: Responds with pre-set logic and conditions. - Agentic AI: Reasons, plans, and adjusts decisions continuously. 6. Response to change - AI Agent: Struggles when inputs or rules change. - Agentic AI: Changes methods and priorities as conditions evolve. If you are building automation, copilots, or internal AI tools, choosing the wrong model limits scale from day one. 👉 Save this for future reference 👉 Repost to help your team understand the difference 👉 Follow Shubham Vora to learn more about building AI agents and implementing in your business.

454

173 Comments

📝 Blogged: "You Gotta Push If You Wanna Pull" Wrote up some thoughts on push vs. pull queries, materialized views, and why data duplication isn't something to fear. (Link to the post in the first comment below 👇)

46

2 Comments

Steal this idea. An API service that exposes a well-designed LLM-as-a-judge. Comparing what items via LLMs is so useful but is non-trivial to get right. I really wished an API service existed on OpenRouter that incorporated all best practices from research. I repeat this may sound simple but getting LLM-as-judge right is non-trivial (position bias, ensembling of models, multiple prompts, rubrics, think step by step, etc.) Given how useful it is to properly compare two items, this service has potential.

165

11 Comments

This looks useful for folks in research or scanning what's coming out on the frontier: Lumi from Google's PAIR research team, a tool for better understanding papers on arxiv. Reading smarter, not harder -- that tagline brings me back to Mortimer Adler's classic 'How to Read a Book'! From their GH repo: Lumi uses AI to help you quickly read and understand arXiv papers. Features include: ✏️ AI-augmented annotations - read summaries at multiple granularities 🔖 Smart highlights - highlight text + ask questions 🖼️ Figure explanations - ask Lumi about images in the paper Demo: https://lnkd.in/ex7erTBb GH repo: https://lnkd.in/emz_Ghcs Medium article: https://lnkd.in/enky8dKC GitHub discussions: https://lnkd.in/efAmiCcp

19

Long Live Spark, Down with Spark 🙃 Spark has been the de facto processing engine for the last 10 years in the "Big Data" community. It still has its place in data processing. Some have branched off their own versions, spun off custom engines, and developed new approaches in an attempt to overcome the shortcomings of spark in diverse landscapes. Our focus is to enable the right compute for the problem with a focus on interoperability with other systems and to have open source compute engines as first-class citizens in platform. Enterprises are highly complex, with high heterogeneity of solutions, tech debt, data gravity, and modalities. I would argue that most pipelines in enterprise are not web scale; they are small and medium sized. A pipeline with a few million or hundreds of millions of rows, sometimes a few billion. Spark is overkill for 90%+ of these workloads and carries a lot of overhead that impacts speed and cost. With the Palantir Multi-Modal Data Plane (MMDP), we provide the framework to store data anywhere, compute data anywhere, and use any model. If we break down this a bit more, this means I can have data spread across different platforms, file formats, structures and use any compute (inside or outside of the platform) to process my data. The permutations of storage locations, formats, and compute engines are massive. It's the right tool for the problem at hand, not one tool you need to shove everything into. Re-platforming should never be the goal. With new frameworks like Polars, DataFusion, DuckDB and the availability of larger node sizes that this means you can run increasingly bigger and more complex workloads in a fraction of the time and cost. You can mix and match these as needed in Foundry and AIP, even having one step run one engine and another step use a different engine. (You can even bring your own engine.) Spark is always there on the table, but it should no longer be the default for most enterprises to start with.

232

24 Comments

I think there's a lot of potential for code gen of algorithmic tasks (like search ranking) ... BUT it's important to aaalways maintain a holdout. Claude wants to overfit to solve the problem. Like listing keywords and applying rules specific to them because it fits one-off query issues, etc https://lnkd.in/epX2atGm

53

1 Comment

I get a feeling that there are discussions going on in a number of boardrooms of late - is this (LLMs) the hill that we're willing to die on? What I expect to see over the next twelve months or so (if the previous couple of decades of "bubbles" are any indication): ► Marketing is going to quietly excise AI from their presentation bullet points ► AI will likely continue in use by individuals, but companies will slow down or curtail broader projects ► AI "Teams" will be disbanded and their members reallocated or released ► There will be a chill in the pace of new initiatives of any sort for some time (12-18 months) as companies step back and "evaluate" their next steps ► Management roles with AI in the title will be rebranded or eliminated ► Budgets that have been AI friendly will be rescoped ► AI Startups that are close to debut will be pushed forward, but those that are less far in the pipeline will be wound down. ► Investors will be far more likely to sit and wait for other opportunities with different technologies. We won't be in an AI winter, but it will definitely be much chillier in this sector for several quarters. ► Expect a lot of proprietary technology to be quietly open-sourced, so that companies can at least keep a finger in the space, but you'll see fewer transformer specific projects. ► There may be a bit of a renaissance for symbolic AI, but I have my doubts it will be strong. That's okay - we always seem to come back to it when things otherwise fall apart. ► The hype machine will likely go quiet for a bit until something else creeps up the Gartner hype cycle. ► GenAI won't go away. We're entering the Trough of Disillusionment, but the best parts of it will likely become infrastructure - known for what it can (and can't do) and incorporated as a significant commodity component, but not necessarily flashy. Some things will be managed better with other technologies, the embarrassing stuff will be quietly forgotten, and we move on. https://lnkd.in/grbuCyM9

113

25 Comments

The best part of being at #Dreamforce is talking to many (MANY!) customers and executives about their biggest challenges in adopting AI agents. The same themes keep surfacing: figuring out where agents actually add value, integrating them into existing workflows, and making sure teams trust the output. My take on it (which has been true for most AI applications for years): 1️⃣ Start with a contained workflow. Don’t “launch an AI agent” . Instead, automate one painful, repetitive process, like summarizing customer calls or drafting support emails. 2️⃣ Focus on clean, connected data before fancy models. Invest in data context. Give your agents access to the right internal docs, CRM notes, and policies and make sure your data is clean, high-quality, and connected. That’s where the real power of agents comes from. 3️⃣ Build trust loops. Let humans review early outputs, collect feedback, and refine prompts and policies before scaling. Focus on the handoff between agents and humans, not replacement. It’s been fascinating to see what’s working (and what’s not) for our customers across many industries. How are you approaching AI agent pilots in your organization? how can we/I help? #salesforce #dreamforce #aileadership #agenticenterprise

111

3 Comments

You're putting out articles, but AI systems are just scraping them into data chunks. They treat your work like a library subset, where the metadata often matters more than the actual writing. No bueno. This disconnect is damaging how content gets licensed and valued. Last time I wrote for Creative Licensing International (CLI), my piece "The Publishing Industry’s Napster Moment" ended up as their best performer so far. Quite proud of that, tbh. As a result, they asked me to do another one. In this latest article for CLI's Content Licensing Brief, "The Commodity Paradox: Why AI Eats Tokens, Not Articles," I dig into the key challenge for publishers and AI labs: why AI platforms license fragments ("chunks") instead of whole articles, and how that changes the leverage for publishers. Think of cases like the The New York Times' lawsuit against OpenAI leading to settlements, or the recent Anthropic agreement to payout $1.5 billion to authors in September 2025, yet publishers still scramble for fair leverage. Even relatively large deals, like Amazon paying the NYT around $20-25 million a year, often feel like scraps compared to the tech giants' gains. If content licensing, AI deals, or rights management is your world (and you're over the vague AI hype), I wrote this thinking of folks like you; practical breakdown, no fluff. Read here: https://lnkd.in/eA3wExuG P.S. If this is your day job, subscribe to CLI's Brief; they convert your IP into a real asset. Things are changing fast, don't get caught out.

26

1 Comment