Mark Hendrickson

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Mark Hendrickson

Mark Hendrickson

@markymark

Building sovereign memory infrastructure for agentic systems w/ https://t.co/fNNyrDlpXF. Previously @LeatherBTC, @HiroSystems, @TechCrunch, @Crunchbase

Barcelona, Spain Katılım Kasım 2007
3.8K Takip Edilen6.4K Takipçiler
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Mark Hendrickson
Mark Hendrickson@markymark·
I've overhauled the Neotoma site. The old single-page wall of text is now a visual presentation backed by full documentation, tool-specific integration guides, and architecture deep dives. All driven by what testers have said or gotten stuck on during the developer release. 🧵
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Mark Hendrickson
Mark Hendrickson@markymark·
This is a bold claim that the new Composer 2 scores higher than Opus 4.6. It does seem to invoke Neotoma more reliably than Composer 1, which often failed to follow MCP and CLI instructions with any consistency. Looking forward to trying it out more, especially given the higher monthly allotment for Composer vs. other models in Cursor. cursor.com/blog/composer-2
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BrandonMarshall.btc
BrandonMarshall.btc@marshallmixing·
Today is my last day at Stacks Labs. After more than 3 years working in @Stacks at Trust Machines (Leather + Granite) and Stacks Labs, I found out this morning that my position, along with 14 others, is being impacted. Here's what's not changing: 1. My connection with all the community members that I have crossed paths with. I've made so many friends in the Bitcoin, Ordinals, and Stacks communities over the years. People say it all the time, but the community in Stacks is absolute top tier. Everyone is mission-driven and truly aligns with Bitcoin's principles. I hope they never lose that and never stop advocating for what's right. It's been a privilege to represent the Stacks community in my role. 2. My focus on Bitcoin. In my 5+ years working in crypto, every position I've held has been a stepping stone toward a role that aligns closer with Bitcoin's fundamentals. I'm looking forward to continuing that trajectory in my next role. If you have a connection, my DMs are open. 3. Good For Bitcoin will continue. I'm excited to continue co-hosting the show alongside Kate. GFB has been a fantastic way for the both of us to keep a pulse on what's going on in the industry. Tomorrow's show will be a good one.
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Erik Voorhees
Erik Voorhees@ErikVoorhees·
It may be obvious in hindsight that we actually built crypto for the machines
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Mark Hendrickson
Mark Hendrickson@markymark·
The notes from @levie's enterprise dinner hit nearly all of the emerging agentic trends I wrote about last month: - Agents moving from pilots to production → stateful economic actors. Once agents run live workflows (not just demos) they accumulate state: credentials, intermediate results, decisions made over time. Resetting them stops being a fix and starts being a cost. - Token budgeting as OpEx → metered usage. Levie says tokens won't make sense as an IT budget line anymore. When token spend is OpEx, redundant inference and context re-derivation become visible waste, not rounding errors. - Data fragmentation blocking agents → platform memory stays opaque. Years of scattered data management weren't a problem when humans navigated between systems. Now agents need to operate across that mess securely, and no single platform is making it transparent or portable. - No single platform winning → tool fragmentation persists. Every enterprise is deploying multiple AI systems. Interoperability becomes the constraint, not which model is best. The problem shifts from interface fragmentation to state fragmentation wherein context lives in too many places at once. - Governance and identity as core challenges → audit drifts down-market. Who can the agent access? What did it do with that access? These questions used to be enterprise compliance concerns. Now they're showing up wherever agents touch real data and real decisions, regardless of org size. The convergence between what enterprise leaders are saying at dinner and what builders are seeing in the architecture is getting hard to ignore. markmhendrickson.com/posts/six-agen…
Aaron Levie@levie

Had meetings and a dinner with 20+ enterprise AI and IT leaders today. Lots of interesting conversations around the state of AI in large enterprises, especially regulated businesses. Here are some of general trends: * Agents are clearly the big thing. Enterprises moving from talking about chatbots to agents, though we’re still very early. Coding is still the dominant agentic use-case being adopted thus far, with other categories of across knowledge work starting to emerge. Lots of agentic work moving from pilots and PoCs into production, and some enterprises had lots of active live use-cases. * Agentic use-cases span every part of a business, from back office operations to client facing experiences from sales to customer onboarding workflows. General feeling is that agentic workflows will hit every part of an organization, often with biggest focus on delivering better for customers, getting better insights and intelligence from data and documents, speeding up high ROI workflows with agents, and so on. Very limited discussion on pure cost cutting. * Data and AI governance still remain core challenges. Getting data and content into a spot that agents can securely and easily operate on remains a huge task for more organizations. Years of data management fragmentation that wasn’t a problem now is an issue for enterprises looking to adopt agents. And governing what agents can do with data in a workflow still a major topic. * Identity emerging as a big topic. Can the agent have access to everything you have? In a world of dozens of agents working on behalf, potentially too much data exposure and scope for the agents. How do we manage agents with partitioned level of access to your information? * Lots of emerging questions on how we will budget for tokens across use-cases and teams. Companies don’t want to constrain use-cases, but equally need to be mindful of ultimate token budgets. This is going to become a bigger part of OpEx over time, and probably won’t make sense to be considered an IT budget anymore. Likely needs to be factored into the rest of operating expenses. * Interoperability is key. Every enterprise is deploying multiple AI systems right now, and it’s unlikely that there’s going to be a single platform to rule them all. Customers are getting savvier on how to handle agent interoperability, and this will be one of the biggest drivers of an AI stack going forward. Lots more takeaways than just this, but needless to say the momentum is building but equally enterprises are acutely aware of the change management and work ahead. Lots of opportunity right now.

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Mark Hendrickson
Mark Hendrickson@markymark·
A month ago I wrote about six agentic trends I'm betting on. One of them: agentic usage will become metered. This week @katiebindley of WSJ covers exactly that. @zapier dashboards tracking token burn per employee. @vercel spending $10k in tokens in a day. Companies asking whether outlier usage is waste or a pattern to copy. wsj.com/tech/ai/ai-tok… The cost question underlying the dashboard: are your agents inefficiently re-deriving the same context every run, or do they have a source of truth? markmhendrickson.com/posts/six-agen…
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Mark Hendrickson
Mark Hendrickson@markymark·
Rousseau said tools corrupt us. Condorcet said they advance us. Hobbes said to control them. Kant said to choose to engage with them. The AI debate is replaying all four. Nietzsche asked the question they missed: what do you become? markmhendrickson.com/posts/what-the…
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Mark Hendrickson
Mark Hendrickson@markymark·
I've added consulting and investing pages to my site. I'm taking select engagements helping teams architect and debug reliable agentic systems. Architecture reviews, technical advisory, system debugging, platform design, product architecture & DX. I only take work where there's real technical overlap with what I build daily, because that's where I'm most useful. markmhendrickson.com/consulting I also periodically invest in the same space. Agent infrastructure, structured memory, programmable payments, and developer tools for AI-native workflows. markmhendrickson.com/investing I'm building Neotoma (structured memory for AI agents) and Ateles (the agentic stack I operate daily). The consulting and investing focus on the same problems from different angles.
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Mark Hendrickson
Mark Hendrickson@markymark·
@johnennis Ah I see, for UI specifically. I assumed you were talking about illustrations or other non-UI visual assets.
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John Ennis
John Ennis@johnennis·
I think a big reason of why people think of Ralph loops too narrowly is that they are not using Gemini Gemini is absolutely fantastic at evaluating images and video So you can set up Ralph loops to iterate on images or video or design This opens tons of doors
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Mark Hendrickson
Mark Hendrickson@markymark·
I accidentally wiped my own production Neotoma database. 6,174 observations down to 84 in one command. Got nearly all of it back because observations are immutable and entity state is derived. Merge -> recompute -> done. markmhendrickson.com/posts/how-i-lo…
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Mark Hendrickson
Mark Hendrickson@markymark·
Indeed! I’m prioritizing 1:1 customer contact a lot more this time around. Even with OpenClaw (which is genuinely interesting), I’m finding a gulf between the online revolutionary buzz and my contacts’ actual usage so far. There are a lot of learnings there. I’d be curious to hear how your friends are using it.
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alex rudloff
alex rudloff@alexrudloff·
@markymark @LeatherBTC Another heuristic worth considering: talk to humans face to face. If it’s something they need and understand and are willing to pay for, do more of that. X narratives are 99% grift. OpenClaw has been fun because my offline friends get it immediately.
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Mark Hendrickson
Mark Hendrickson@markymark·
I chased the Ordinals wave at @LeatherBTC because the external narrative urgency felt indistinguishable from real demand. Three years later, the same pattern followed me into AI. I wrote about what FOMO looks like from the inside, and what I'm looking to do differently now. markmhendrickson.com/posts/chasing-…
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Mark Hendrickson
Mark Hendrickson@markymark·
I agree PMF is the real test and all Bitcoin L2s face similar headwinds. The pattern isn't unique to Stacks. As for structure, the issue isn't equity vs. token per se. Liquid ownership always distorts product decisions (e.g. public companies go private for turnarounds when the price signal overwhelms strategy). But post-PMF, usage data at least provides a counterweight. The dangerous combination is liquid ownership before PMF. There's no product signal to compete with the price, so narrative fills the vacuum. Focused leadership matters. But it can't fix a broken feedback loop when price is the loudest signal and there's nothing to counterbalance it.
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HowardA.btc
HowardA.btc@AschwaldHoward·
@markymark @mas44558282 PMF is tough. Let's see how many apps can get real traction. With centralized tradfi entering the use space, that's competition. Self-custody on Bitcoin use cases will be niche compared to CEXs and Wall Street (STRC), imo. However, the other chains are in a worse position.
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Mark Hendrickson
Mark Hendrickson@markymark·
I joined Blockstack in 2018 for the developer tools. Seven years later I left with a clear picture of what happens when token dynamics replace product feedback loops. Not a failure of people. A failure of incentive structure. markmhendrickson.com/posts/when-the…
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Mark Hendrickson
Mark Hendrickson@markymark·
"Institutional-grade intelligence must find the signal, it must structure the noise to cut through slop, and it must be defined, deterministic, and auditable in the work it does." The seven pillars in this AI enterprise article all presuppose something it doesn't name explicitly: a shared memory substrate. Coordination requires agents that share state. Signal requires deterministic, auditable checkpoints. Bias correction requires ground truth that doesn't bend to the user. Unprompted action requires persistent context. Every pillar assumes a truth layer underneath it. Individual AI stores memory in ephemeral context windows. Institutional AI needs memory that persists, compounds, and can be verified across agents, humans, and time. That's the infrastructure gap between "we have electricity" and "we've redesigned the factory." The factory needs a foundation.
George Sivulka@gsivulka

x.com/i/article/2024…

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Mark Hendrickson
Mark Hendrickson@markymark·
The positive signal from testers: the core works. It stores and retrieves correctly. But the site and onboarding needed to catch up. This overhaul is a first step in that direction. If you try the site or install, I want to know: is the positioning clear? Can you get from the home page to a working setup without hitting a wall? Full writeup of updates with screenshots: markmhendrickson.com/posts/neotoma-…
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Mark Hendrickson
Mark Hendrickson@markymark·
I've overhauled the Neotoma site. The old single-page wall of text is now a visual presentation backed by full documentation, tool-specific integration guides, and architecture deep dives. All driven by what testers have said or gotten stuck on during the developer release. 🧵
Mark Hendrickson tweet media
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