Charlotte Kleverud

@lewiscarhart If you’re so much better - which I thought you were, you’re great at marketing! - it’s just ungraceful to bash a smaller competitor

@lottite It be like that sometimes

This guy is such a fucking dweeb Brother you’ve been building OneLeet for 5 years and it sucks Focus on your incredibly high churn, not our dumb Reddit marketing ❤️

@lewiscarhart This is just mean…

OneLeet is not a serious platform. No device agent Manual screenshots Less than 100 integrations Terrible policies and vendor management (Your ex customers have shared screenshots with us) I laughed out loud when I saw you guys managed to convince someone to invest $30M

@eringriffith forbes 30 under 30 strikes again

A detailed and brutal look at the tactics of buzzy AI compliance startup Delve "Delve built a machine designed to make clients complicit without their knowledge, to manufacture plausible deniability while producing exactly the opposite." substack.com/home/post/p-19…

@AdamDraper I like Gail

accidently typed in gail.com instead of gmail, and its my new favorite website.

@big_duca Came across this today. For comparison: Google employees in Sweden make on average 190k per year

Man if you’re in tech, you better be in the US. I was curious so I looked at a job posting. 10 year experience staff engineer. Based in Europe. Limited PTO. And my comp for my first job when I was 19 was 3x the listed salary.

@briannekimmel 🙋‍♀️

Hosting some unexpected, quirky, uniquely SF experiences over the coming weeks. If you're new to the city or want to meet new people outside of work, please DM me.

I wish there was a more established scale of agency; in my world real agents can proactively figure out how to solve problems and even what problems to solve, coordinate with agents and humans autonomously, use tools and apps like a human to drive outcomes, learn from their mistakes and improve. But that bar seems pretty high

agents (1) automating one off tasks vs (2) orchestrating 20 day processes across 5 people and 8 systems are fundamentally different systems design problem. imo the latter is far more interesting right now. single player // multi player

@petergyang @andrewchen Agree with the latter but OKRs - didn’t they work because of politics and optics ie… humans? I’m finding them quite useful for agents since they are, in their essence, just goals and their success criteria

@andrewchen Hot take: OKRs, standup, and this waterfall crap never worked anyway :)

in a world of agents, the product role is going to split into two jobs: - one that organizes humans (stakeholders, design, eng) - one that organizes agents (prompts, evals, workflows, etc) Both will be in pursuit of offering the right products to customers, but how you get there will dramatically change. What happens to the typical product rituals? Instead of PRDs, OKRs, standups, product reviews, we'll need the equivalent for agents. Couple wild ideas here... instead of standups: the equivalent is that agents will report back to us based on run logs and anomaly flags. no one needs to say what they did yesterday, the system already did thousands of things. the question is where it broke, where it surprised you, and where it got better. Show us the patterns, the trends, the edge cases - particularly the ones the agents didn't fix automatically. the daily ritual becomes reviewing deltas, scanning failures, and deciding which ones matter. less reporting, more triage instead of OKRs: we’ll need adversarial agents that continuously monitor/grade the system and detect patterns, scoring outcomes on an hourly or daily basis. Rather than setting a quarterly goal of "increase X by 5%" and revisiting slowly -- instead, management will be able to monitor success in real-time and detect trends/patterns towards overall goals instead of PRDs: we won't need waterfall. Prototyping will rule the day, and we’ll need a living agentic loop that mediates customer feedback/ratings and what's being prioritized and built. you don’t hand it to eng, you deploy it into the agent loop. if it’s wrong, it fails visibly and you can revert. if it’s right, it produces the right output instead of product reviews: we'll need simulation systems to examine agent behavior in different scenarios. In an agentic world where UI shifts from buttons/menus to agents automatically doing things, you'll want to examine their behavior before you deploy. You rewind decisions, fork alternate paths, and see how different prompts or constraints would have changed outcomes. the review becomes interactive. less storytelling, more counterfactuals. The PM sits in the middle of this split. On the human side, still aligning taste, risk tolerance, and strategy across people. On the agent side, shaping the actual behavior of the system through prompts, evals, and feedback loops. one side is persuasion. The other is instrumentation. the best ones will collapse the gap, translating intent directly into systems that act on it. the fascinating part is that the agentic loop will run 10000x faster than the human one, and of course, you can "hire" them faster. Thus the “organizing humans” half starts to feel slow and lower impact unless it directly improves the agent loop. Eventually the PM will shift towards agents and maybe ignore the human coordination altogether...

@CoastalFuturist Interested!

If there’s enough interest I’d like to make a group chat for people using openclaw / hermes agent heavily I really want to understand some good use cases, best practices, and just have a place for people to talk shop Comment if you’re interested

@startupideaspod Oh this sounds like @momentalos

It’s been three strange years for product people > 2023: become an AI product manager, whatever that is > 2024: vibe code or you won’t make it > 2025: Claude code or you won’t make it > 2026: everyone is a product manager anyway - this one is for everyone new to product

@dmitry140 1000 is the new 100k. 100 is the new 10k

no company founded >2026 will organically grow to >10k employees

Management is planning and coordinating to achieve goals. It’s exactly what agents need and it is not dead; rather, we need to figure out how to manage more entitities than the usual headcount a manager has

@rryssf_ “The part nobody has solved yet: When one agent updates shared memory, the other agent has no way of knowing when that update is visible or what happens if both write conflicting information at the same time.” -> we’re solving it in @momentalos

🚨 BREAKING: AI agents can't share memory without corrupting it. Here's why every multi-agent system being built right now is sitting on a time bomb: > When two AI agents work on the same task, they share memory. One reads while the other writes. Sometimes simultaneously. And there are zero rules governing any of it. > Computer scientists solved this exact problem in the 1970s. They called it memory consistency. Every processor, every operating system, every database runs on it. AI agents skipped the memo entirely. > We built entire multi agent frameworks AutoGen, LangGraph, CrewAI without a single consistency model underneath them. The result: > agents overwriting each other's work > reading stale information and treating it as fact > producing conflicting outputs with zero awareness that a conflict exists UC San Diego mapped the fix using classical computer architecture as the blueprint: > three memory layers (I/O, cache, long-term storage) > two critical missing protocols: one for sharing cached results between agents, and one for defining who can read or write what and when The part nobody has solved yet: When one agent updates shared memory, the other agent has no way of knowing when that update is visible or what happens if both write conflicting information at the same time. Every multi agent system in production today is running without these rules. That's not a future problem. That's the current state of the entire industry.

@carlhua Hey, we never thought of ourselves as an IDE - we’re building @momentalos where you assign agents (like Claude code) to an objective that it breaks down into success metrics, problems to solve and features to build based on your goals and context happy to chat

i used to write flight software - you know, the one that flies jets and spacecrafts.. and if I had claude/codex back in the day, i wouldnt even need to look at code. hear me out, people say AI slop this, AI slop that, and the code it generated is trash etc etc. the thing is, with a highly tightened coding standard, and a requirement traceability down to say ~30-50 lines of code per requirement, you almost no longer need to look at the code it produced before doing testing. 2022 - oh wow, AI can write some code in the chat app 2023-2024 - cursor is amazing - it can write some code and understands the context 2025- the code is getting better and better, but often with mistakes 2026 - given the right guideline and specs, it is an EXPECTATION that the code should work the first or second round. I foresee in the near future, we would have IDE that no longer prioritizes displaying code. instead the IDE would be a tool to orchestrate agents, with responses gathering and display, so like an agent command center. I already see people building some of this but i think people are thinking about it wrong - don't take IDE or terminal as a baseline, we need to completely revamp it. This excites me because for once, we are going to revolutionize how coding is done. wait.. its not coding anymore, its more general than that, but you get the point! the subagent framework today is largely inadequate - we need a layer of LLMs on top of analyzing agents and then allow humans to control. but not all subagents should report back to the same upper LLM. whos building this? I will invest.

@norootcause It’s like listening to someone who wants to tell you about the dream they had last night

Nobody wants to read the output of someone else’s prompt

We negotiated our rental lease like this last year - shared the prompt and the output, pointing out where the contract was unreasonably strict. Since we shared the prompt, our landlord could see that we weren’t biased but instead aimed to make it hold up in Californian court in case of a dispute

soon you won't prep a deck for a team meeting to make a business decision instead you'll prep and discuss a prompt - one that susses out the right parameters/inputs driving the outcome the result of the meeting will be: - aligning on the prompt - feeding it into a simulation engine -> getting the ultimate recommendation

@dazzeloid remember when we hid using it at work because it was cheating

@daviddorg @claudiaroussel_ Taking note that being blamed for being ai is the highest level of compliment (unless it’s about writing, in which case it’s an insult)

Confirmed: @claudiaroussel_ is not AI