Mikko Kiiskila

Product Builder. Dog dad. Here now, buidling hobby project Yodai in public.

[Sebastian Caliri @SebastianCaliri]

I just tested my hand in a mini version of this scanner. Images that are higher quality than MRI, whole body captured in <1 minute, virtually free to run. This is going to change medicine.

Things get even crazier when you consider the possibility of using the same tank to focus ultrasound to ablate tissue, stimulate nerves, etc.

The FDA is not in the slightest ready for this. People will also complain about incidental findings but they are wrong and don’t understand how quickly software can improve and how inexpensive a time series of scans will be to generate.

[Mikko Kiiskila @Kibu3000]

@jarredsumner Impressicd! How many hands on turns did you make during theee 6 days?

[Jarred Sumner @jarredsumner]

Dynamic workflows and adversarial code review was part of what made it possible to rewrite Bun in Rust in 6 days.

[Mikko Kiiskila @Kibu3000]

@ericzakariasson Love it. Though setting up self driving end to end idea-&lt;deploy loops is still a bit glitchy despite several hooks to Cursor SDK. Primarily premature stops on having the loops run themselves on repeat.

[Patrick Collison @patrickc]

Worth watching. Preview of the future.

[Cursor @cursor_ai]

We’re introducing the Cursor SDK so you can build agents with the same runtime, harness, and models that power Cursor.

Run agents from CI/CD pipelines, create automations for end-to-end workflows, or embed agents directly inside your products.

[Patrick Collison @patrickc]

We just launched the @Link CLI: https://t.co/vYdvNtJgpE. Tell your friendly neighborhood agent about it -- agents can use the Link CLI to create single-use credentials that you get to synchronously approve each time.

I asked Claude to buy itself a gift. It chose HTTPZine on Gumroad.

[Claude @claudeai]

With the Autodesk Fusion connector, designers and engineers can create and modify 3D models through conversation.

[Lenny Rachitsky @lennysan]

My biggest takeaways from Claude Code's Head of Product @_catwu:

1. Anthropic’s product development timelines have gone from six months to one month, sometimes one week, sometimes one day. Part of this acceleration is access to the latest models (i.e. Mythos). Another is shipping new products into “research preview,” making clear it's early, experimental, and might not be supported forever. Another is an evergreen "launch room "where engineers post ready features and marketing turns around announcements the next day.

2. The PM role is shifting from coordinating multi-month roadmaps to enabling teams to ship daily. As Cat puts it, “There should be less emphasis on making sure you are aligning your multi-quarter roadmaps with your partner teams and more emphasis on, OK, how can we figure out the fastest way to get something out the door?”

3. The most efficient shipping unit is an engineer with great product taste. On Cat’s team, many engineers go end-to-end—from seeing user feedback on Twitter to shipping a product by the end of the week—without a PM involved. Also, almost all the PMs on the Claude Code team have either been engineers or ship code themselves, and the designers have been front-end engineers. The roles are merging, and the most valuable skill is product taste, not job title.

4. Build products that are on the edge of working. Claude Code’s code review product failed multiple times because earlier models weren’t accurate enough. But because the prototype was already built, they could swap in Opus 4.5 and 4.6 and immediately test whether the gap was closed. Teams that wait for the model to be ready will always be a cycle behind.

5. The most underrated skill for building AI products is asking the model to introspect on its own mistakes. Cat regularly asks the model why it made an unexpected decision. The model will explain that something in the system prompt was confusing, or that it delegated verification to a subagent that didn’t check its work. This reveals what misled the model so the team can fix the harness.

6. Every model release forces their team to revisit existing products and audit their system prompt to remove features the model no longer needs. Claude Code’s to-do list was a crutch for earlier models that couldn’t track their own work. With Opus 4, the model handles it natively. Features built as scaffolding for weaker models become debt when the model catches up—so the team actively strips them.

7. Anthropic employees build custom internal tools instead of buying SaaS products. A sales team member built a web app that pulls from Salesforce, Gong, and call notes to auto-customize pitch decks—work that used to take 20 to 30 minutes now takes seconds. Their core stack is Claude Code, Cowork, and Slack. No Notion, no Linear, no Figma.

8. People underestimate how much Claude’s personality contributes to its success. As Cat describes it, “When you reflect on everyone you’ve worked with, there’s just some people where you’re like, I really like their energy, their vibe.” Claude is designed to be low-ego, positive, competent, and earnest—qualities that make it feel like a great coworker, not just a tool. This isn’t cosmetic; it’s what makes people want to use Claude for hours every day. The team has a dedicated person, Amanda, who “molds Claude’s character,” and it’s one of the hardest roles at the company because success is so subjective.

9. The future of work is managing fleets of AI agents, not doing the work yourself. Cat sees a clear progression: first, individual tasks become successful. Then people start running multiple tasks at the same time (multi-Clauding). Next, people will run 50 or 100 tasks simultaneously, which will require new infrastructure—remote execution, better interfaces for managing tasks, agents that fully verify their work, and self-improving systems that incorporate feedback. The human role shifts from doing the work to knowing which tasks to look into, verifying outputs, and giving feedback that makes the system better over time.

10. Hire people who lean into chaos and face every challenge with a smile. At Anthropic, there are weeks when a P0 on Sunday becomes a P00 by Monday and a P000 by Monday afternoon. If you get too stressed about any one thing, you’ll burn out. Their team looks for people who can look at a hard challenge and say, “Wow, that’s gonna be hard. But I’m excited to tackle it and I’m gonna do the best that I possibly can.” This mindset—optimism, resilience, and comfort with constant change—is increasingly essential as the pace of AI development accelerates.

Don't miss the full conversation: https://t.co/1wOUHcdYQN

[Mikko Kiiskila @Kibu3000]

So when he saying about predicting on what happens next with world models I understand the scope on video based models to understand consequences of actions in mechnaics and movement but what about the social and human interaction decision based consequences that are largely changes n the human behavior side that the large language models are somehow trying to do maybe? Is this somehow in scope of the world models as and if so then how?

[Anthropic @AnthropicAI]

New Anthropic research: Emotion concepts and their function in a large language model.

All LLMs sometimes act like they have emotions. But why? We found internal representations of emotion concepts that can drive Claude’s behavior, sometimes in surprising ways.

[Boris Cherny @bcherny]

Today we're excited to announce NO_FLICKER mode for Claude Code in the terminal

It uses an experimental new renderer that we're excited about. The renderer is early and has tradeoffs, but already we've found that most internal users prefer it over the old renderer. It also supports mouse events (yes, in a terminal).

Try it: CLAUDE_CODE_NO_FLICKER=1 claude

[Boris Cherny @bcherny]

3/ Two of the most powerful features in Claude Code: /loop and /schedule

Use these to schedule Claude to run automatically at a set interval, for up to a week at a time.

I have a bunch of loops running locally:

- /loop 5m /babysit, to auto-address code review, auto-rebase, and shepherd my PRs to production
- /loop 30m /slack-feedback, to automatically put up PRs for Slack feedback every 30 mins
- /loop /post-merge-sweeper to put up PRs to address code review comments I missed
- /loop 1h /pr-pruner to close out stale and no longer necessary PRs
- lots more!..

Experiment with turning workflows into skills + loops. It's powerful.

https://t.co/cx6YMibGN3

[Cheng Lou @_chenglou]

My dear front-end developers (and anyone who’s interested in the future of interfaces):

I have crawled through depths of hell to bring you, for the foreseeable years, one of the more important foundational pieces of UI engineering (if not in implementation then certainly at least in concept):
Fast, accurate and comprehensive userland text measurement algorithm in pure TypeScript, usable for laying out entire web pages without CSS, bypassing DOM measurements and reflow

[Cursor @cursor_ai]

Cursor cloud agents can now run on your infrastructure.

Get the same cloud agent harness and experience, but keep your code and tool execution entirely in your own network.

https://t.co/u3iVnZxtKV

[Boris Cherny @bcherny]

New in Claude Code: Code Review. A team of agents runs a deep review on every PR.

We built it for ourselves first. Code output per Anthropic engineer is up 200% this year and reviews were the bottleneck

Personally, I’ve been using it for a few weeks and have found it catches many real bugs that I would not have noticed otherwise

[Patrick Hsu @pdhsu]

Evo 2, our fully open-source biological foundation model trained on trillions of DNA tokens spanning the entire tree of life, is out in @Nature today

We &amp; the scientific community have done a lot with this @arcinstitute @nvidia model in the last year! 🧵👇

[Brian Armstrong @brian_armstrong]

The next unlock for AI agents just launched.

@CoinbaseDev released agentic wallets, the first wallet infrastructure designed for AI agents.

Now agents can spend, earn, and trade autonomously and securely.

[Aakash Gupta @aakashgupta]

57% of merged PRs at Ramp in the last 24 hours came from a background agent. Most companies haven’t even started.

The architecture Ramp built matters. Their agent Inspect runs in sandboxed VMs on Modal with full access to everything a Ramp engineer has: Sentry, Datadog, GitHub, CI/CD, feature flags, databases, live preview environments. The agent doesn’t just write code. It runs tests, checks telemetry, verifies frontend changes with screenshots, and opens PRs that pass the same review bar as human-written code.

This is why the number is so high. Model intelligence was already sufficient. Environment parity was the missing piece. Once agents have the same context and tooling humans have, adoption compounds because engineers stop treating the agent as a side tool and start treating it as a parallel teammate running unlimited concurrent sessions.

The product development implications are massive. PMs at Ramp now use Inspect during QA to make changes in real time instead of writing tickets. Designers can ship fixes without waiting for sprint capacity. The marginal cost of implementing a small change drops to near zero, which means the backlog starts to dissolve.

Most engineering orgs are still debating whether to adopt Cursor or Copilot. Ramp already moved past the foreground agent phase entirely. Background agents that run autonomously, verify their own work, and produce merge-ready PRs at scale.

The gap between companies measuring their agent PR ratio and companies that haven’t built the infrastructure to support one is going to define the next era of product velocity.

[Dan @robustus]

Turns out with claude code, my decades long strategy of NOT deeply learning:
- regexs
- sql
- nginx confs
- elaborate shell commands
- advanced shell scripting
- any javascript framework
- perf optimization
- webpack, cdns, bundlers
- 1000 other things

...was entirely correct.