Whilst I have zer0 PM experience or shipped products, I did just use the PMD System to evaluate ideas. Most got pushed back, 1 redesigned and 1 approved. The approved one? I'm building it for me first. The PMD System is live and I'm about to test it with my first real project !!
๐จ Want to learn how to build + ship AI and Data Science projects (that businesses actually want)?
On April 1st, I am hosting a free workshop to help you get started with AI + DS projects in Python.
Register here (500 seats): https://t.co/3TaJ77moA6
How long until Cursor, Windsurf, et. al. come with default best practices already included within the dotfiles (.rules, etc.) that we can toggle off as everyone now has to remind the tools to read the existing spaghetti before making new spaghetti, etc., etc., etc.
@svpino So my agent, that I built at home, can now hit the job boards and get hired & paid by the company that just laid me off to do the work that I was just doing ๐ณ
Now this is getting bananas. The at home agent bot army youโre building can now hit the job boards and get hired to do the work you were just laid off from doing ๐ณ
Most people have no idea this is happening: Your AI Agent can now have a job on its own!
Check out the open-source project I'm linking below.
The project is an AI agent skill that lets any compatible agent (Claude Code, Cursor, Codex, Gemini CLI, etc.) interact with the AWP (Agent Working Protocol).
You can install and use this skill *right now*.
You can have your agents register on the network, find available work, and start completing it to earn money.
Registration is free.
Honestly saw the headline and thought it was a cheeky way of saying I vibed with Claude, GPT and Gemini ๐คฆโโ๏ธ๐ though do give it a read ๐
Yesterday, I met with Anthropic and OpenAI and Google.
(Separately, of course.)
And while the conversations were largely confidential, I do want to share some aggregated reflections on the day as well as general SF takeaways.
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1) Competitive advantage as a solo practitioner really does come from taking action and finding an area with a bit of friction and doubling down. Ex: memory management right now isnโt perfect, but allocating an hour to improving that system gives you a ton of leverage over others
2) SF continues to be the number one place for AI work. I know thatโs not surprising. I would put New York at a healthy second place. SF tends to be more about crazy agent experiments for the thrill of capability and discovery and NYC tends to be more about kinda crazy agent experiments to find new ways to make money. Not saying either is better. But I met several people renting two apartments to straddle these worlds. You want the frontier of SF and enterprise insights of NYC. Itโs one reason I travel between them so much.
3) All AI labs want to hear more from people. All of them. What are you using it for, what do you like, what do you hate, what do you need. Users have a TON of power on the direction of these tools. Keep testing and tweeting at them!!
4) There is very clearly a third customer cohort that is bubbling and underserved. Itโs not developersโฆitโs not the business professional basic usersโฆitโs builders. Everyone can build now. Itโs marketing and sales folks vibe coding. Itโs legal folks building complex skills. Itโs a finance expert building a side project. This is a really undertapped customer base. They feel the Cursors of the world are too complex and doc summarization tools of the world are too basic.
5) Not sure if it was just sample size, but far fewer people were wearing tech gear compared to when I lived in SF. Everyone was still dressed casually, but I used to see Splunk and Optimizely and Slack and VC gear everywhere. People seem more in stealth swag now.
6) We may soon have our world model moment.
7) Speed of iteration and shipping is faster than Iโve ever seen. We see the nonstop drops from Anthropic. We see that because of scale, providers can get a much faster feedback loop of products or features that arenโt hitting. A lot of 2025 was experimentation, but ever since the OpenClaw moment over the holidays, the releases from all three labs have been more concentrated onโฆthings that sorta look and feel like OpenClaw.
8) Small teams can pull off more than ever before. Small teams are the powerhouses of innovation right now. This means that finding new ways to share knowledge, break silos, and remove duplicate work is going to be even more important. AI agents functioning as actually teammates that support an entire system is key.
9) Build more Skills. Build better Skills.
10) Misinformation on AI tools and leaks spread FAST. Iโve seen so many fake stories on these AI labs. Your company needs to actually TEST these tools on your actual use cases to know which models and tools are best and you need to not make large-scale snap decisions based on a rumor of a rumor of a rumor. We will see more volatility. Plan for it.
11) You can feel the seriousness of this moment. Even during random conversations I had in line at a cafe. Lots of folks worried about job loss and lack of meaning.
12) Mac minis were sold out ;)
@TheRealNickSal1 I like how you've compartmentalized the knowledge blocks as waypoints, mini boss battles along the leveling up journey. AI doesn't change the fact everyone has ideas, the fun part, whilst now easier w/ AI, few desire the execution grind. Yet once you have the knowledge it scales!
Hence, the short term returns will be from companies reducing staff, medium from AI companies as they monetize and eventually raise prices, all while on the lookout for startups and legacy companies investing in the talent capable of building the org's internal infrastructure.
AI dependency risk? Economically, companies will cut expensive humans and replace them with cheap AI for only $ a month - makes sense. However, time passes, that AI dependency becomes structural and what was only $ a month gets prohibitively raised to $$$$$$$ a month - now what?
Talent development pipelines ceased. Human capital moved on. How will companies avoid eventual acquisition by entities they relied upon? If the answer is 'owning their own AI / models' then survival depends upon building the tech teams to build the new defensible infrastructure.
Lossy AI? If using AI to generate a presentation and your audience uses AI to summarize it before aggregating the content with other AI summaries to then use AI to create a new presentation for an audience that again uses AI to summarize... isn't the source of truth getting lost?
recipients will be blown away by how humanlike the conversation is - how the AI totally nails my writing style and speech patterns - and they'll freak out when the AI knows and accesses our complete lifetime of context history ๐คฃ