John Bourous @jbourous

We’ve just launched our Signals MCP server, it’s now even faster to integrate customer context into your agents. Agents can now decide what context is most useful when carrying out a task. So you don’t waste unnecessary tokens. Read more here: signals.snowplow.io/blog/signals-m…

Feature stores never ask whether the ID you hand them is a whole person or a single browser's view of one. Your model learns from the fragmented view of a person and scores them with complete confidence. Read our latest blog post about our upcoming Signals and Identities integration to see what we are solving for: signals.snowplow.io/blog/feature-s…

Should this subscriber see an offer before they cancel? Is this visitor mid-task, or is now a good time to ask? Most apps answer with a hardcoded rule or a complex ML model that took months to build. A System One model can answer from what the user just did. @matus_tomlein shares his thoughts below: signals.snowplow.io/blog/ask-a-sys…

@ttunguz agreed with @kevindp51 sentiment below. Touched upon the subject last week, specifically about context, but in general agreed the harness seems like the moat. x.com/jbourous/statu…

John Bourous @jbourous

3 weeks ago

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@OpenAI with all these advancements in decisioning and UI rendering lately, user context (especially real-time) seems like only moat left? x.com/jbourous/statu…

John Bourous @jbourous

3 weeks ago

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@_awchen Exciting stuff @_awchen! Seems like context and speed are becoming key ingredients to these experiences. Wrote a bit about it here - curious to get your thoughts x.com/jbourous/statu…

John Bourous @jbourous

3 weeks ago

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Welcome to the age of real-time agentic personalization! Your users tell you what they want with every click. Now a model can read that stream, work out their intent mid-session, and change the experience live. Coming soon to Signals.

@perplexitydevs I already need to update my article to reflect @perplexitydevs entering the decisioning ring! x.com/jbourous/statu…

John Bourous @jbourous

3 weeks ago

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We have launched a free forever tier for Signals. No more time-caps. Up to 5 million/events per month. Build something today. signals.snowplow.io/start

Want to see exactly how Signals can deliver real-time customer context in your stack? Try out our new architecture fit tool. Enter details about your current stack, and get back an architecture diagram, further details, and a prompt to get you started. signals.snowplow.io/architecture-f…

Build generative UI that adapts to each users in real time. See below an example of it in action. The store has no fixed pages. Signals provides the live context, Jev makes the decisions, json-render draws the components. Signals x @typesafeai Jev x @vercel json-render

@DavidPawlan @alexcrdean ^

@jonaswillett1 Cafe

@AIandTechh87 COWORK

@JJEnglert GUIDE please- great overview!

@henrythe9ths AI Guide

There's a shocking fact about AI that nobody tells you: You can catch up to the public AI research frontier in just 2 weeks. Yes, really. I've built a $150M annual revenue startup over the last 8 years and If I were to start a company today, I’d drop everything and go all-in on AI. But like many busy software builders, I felt lost—overwhelmed by the noisy, crowded and fast-moving modern AI landscape. And I wasn’t alone. So I spent my entire holiday diving deep into AI research—reading 30+ papers, watching hours of lectures, analyzing trends, and catching up to the research frontier. ✨ Here’s what I learned: - You don’t need months (or years) to catch up. - You don’t need a PhD or decades of ML experience. - You need fewer than 20 papers and 2 weeks to understand the major breakthroughs shaping AI today. It's because the technology is extremely nascent and most techniques that came before are no longer relevant: - ChatGPT is barely 2 years old and Transformers are only 7 years old. - Most game-changing discoveries happened within the last 4 years, driven by a few breakthrough ideas, scaling laws, and efficient matrix multiplication. The biggest secret? Many groundbreaking AI papers with thousands of citations are surprisingly simple and applied, like adding "let's think step by step" to the prompt, or simply asking the LLM over and over again to improve its answer (Self-Refine). I realized there are tons of founders and builders in the same boat—wanting to dive deeper into AI but unsure where to start. I've created an essential AI Guide that helped me catch up, in just 2 weeks, to the frontier of public AI research to figure out where the next opportunities and gaps were: - Curated list of only the most important papers - Simple explanations of key concepts - Clear pathway to understanding the frontier of modern AI It’s perfect for: - Founders expanding into AI - Builders wanting to innovate at the frontier of AI - Investors looking to separate the signal from the noise 👇 Want the full guide? - Like and Share this post - Comment "AI Guide" - I'll send you the complete guide (ps, I’m also teaming up with @VishalVasishth, co-founder of @obviousvc with @ev (focused on large-scale societal impact companies like Twitter, Medium, Beyond Meat), to host a small meetup to discuss what's working and needs to be solved in the AI stack in SF. Message me if you're interested)

@TheM_A_L

@JimDaily @LinksDAO 👍🏻