Daniel Lin

Agentically Engineering AI + Crypto Products

[Daniel Lin @Pofrandom]

Those people also proved @steipete is correct about CLI

[ThePrimeagen @ThePrimeagen]

never forget

The people who tell you coding is a solved problem also created MCP

[Daniel Lin @Pofrandom]

CLI

[Minds by Animoca Brands @hellominds_]

If you build with Claude Code, Codex, Cursor, Aider, or Lovable, this is for you.

Minds CLI helps you start building on Minds by @animocabrands with less setup friction.

Bring the skill idea.
Let your coding agent handle the setup.

Start building with Minds CLI: https://t.co/koFDaKRjzm

Full prompt below 🧵

[Daniel Lin @Pofrandom]

😂

[shirish @shiri_shh]

very few people know that Anthropic CEO played the role of Dan Melcher in the Silicon Valley.

[Daniel Lin @Pofrandom]

@anuraggoel no brainer

[Daniel Lin @Pofrandom]

@realmadhuguru Builder PM is the way to go

[Daniel Lin @Pofrandom]

@jonathan_wilke Because its for agents, not humans....

[Cursor @cursor_ai]

It’s now easier to move local agents to the cloud so they can keep working with your laptop closed.

Prompt Cursor from your phone, run many agents in parallel, and get back PRs with demos of their work.

[Daniel Lin @Pofrandom]

@petergyang sym link seems to work

[Daniel Lin @Pofrandom]

@ai_for_success I am on Pro, also hitting rate limits like never before.

[Andrej Karpathy @karpathy]

Personal update: I've joined Anthropic. I think the next few years at the frontier of LLMs will be especially formative. I am very excited to join the team here and get back to R&D. I remain deeply passionate about education and plan to resume my work on it in time.

[Pau Labarta Bajo @paulabartabajo_]

Advice for AI engineers 💡

Stop building LangGraph pipelines.

Modern LLMs are so good at tool calling that your hand-crafted orchestration is actually hurting performance.

Let the model decide.

[Daniel Lin @Pofrandom]

@paulabartabajo_ spot on!

[Mo Ezeldin @Mo_Ezz14]

Honoured to be attending the Transforming Global Education Summit at the @UN in NYC

Seeing so many innovative minds, governments, Education providers and young leaders in one room with a single mission - redefining education as global infrastructure.

Will be sharing what we are building at both Open Campus (Education operations system) and Minds (accessible AI infrastructure for the masses) and how this can help elevate the education experience and outcomes for those involved

If you are around, please do find me for a chat!

[Viv @Vtrivedy10]

Cursor is a great example of why tuning the harness matters because "same model, diff harness, diff performance"

main takeaway is every agent can be harness engineered to be better at a particular set of tasks and capabilities you care about

very rarely will a default base harness be optimal at your task, the main reason why is because of how many of the best teams build agents today:
1. teams build harnesses/agents by choosing a set of tasks to ground the design for v0 of the agent, ideally even this v0 is grounded in evals

2. through dogfooding and eval design, they shape and change the agent to make the evals pass or improve the dogfooding experience. things like changing prompts, adding tools/skills, encouraging subagent use, etc

3. they also update the evals as they find new important use-cases and issues in the agent. then they again continue editing the agent to be in line with passing the set of tasks/evals

4. but your exact set of tasks and their tasks basically never fully align - they're a rough proxy of what you want and even more so their evals are a rough proxy of they want! practically this means you can almost always extend a base harness or choose different combinations of models to get a bit more perf by better fitting it to your tasks/evals

and this is how we get great stories and reports of different builders building better agents/harnesses than the model providers themselves for certain tasks, ty @d4m1n :)

[Daniel Lin @Pofrandom]

@hoomanask @jonathan_wilke @cursor_ai not since composer 2 came out. ultra plan is almost too much.

[Daniel Lin @Pofrandom]

@theCTO https://t.co/gm5kxDk3Y0 super underrated

[Daniel Lin @Pofrandom]

@real_joe_di @AndrewYNg or worse the corporate minded might actively try to slow down those highly productive AI ones

[Andrew Ng @AndrewYNg]

AI-native software engineering teams operate very differently than traditional teams. The obvious difference is that AI-native teams use coding agents to build products much faster, but this leads to many other changes in how we operate. For example, some great engineers now play broader roles than just writing code. They are partly product managers, designers, sometimes marketers. Further, small teams who work in the same office, where they can communicate face-to-face, can move incredibly quickly.

Because we can now build fast, a greater fraction of time must be spent deciding what to build. To deal with this project-management bottleneck, some teams are pushing engineer:product manager (PM) some teams are pushing engineer:product manager (PM) ratios downward from, say, 8:1 to as low as 1:1. But we can do even better: If we have one PM who decides what to build and one engineer who builds it, the communication between them becomes a bottleneck. This is why the fastest-moving teams I see tend to have engineers who know how to do some product work (and, optionally, some PMs who know how to do some engineering work). When an engineer understands users and can make decisions on what to build and build it directly, they can execute incredibly quickly.

I’ve seen engineers successfully expand their roles to including making product decisions, and PMs expand their roles to building software. The tech industry has more engineers than PMs, but both are promising paths. If you are an engineer, you’ll find it useful to learn some product management skills, and if you’re a PM, please learn to build!

Looking beyond the product-management bottleneck, I also see bottlenecks in design, marketing, legal compliance, and much more. When we speed up coding 10x or 100x, everything else becomes slow in comparison. For example, some of my teams have built great features so quickly that the marketing organization was left scrambling to figure out how to communicate them to users — a marketing bottleneck. Or when a team can build software in a day that the legal department needs a week to review, that’s a legal compliance bottleneck. In this way, agentic coding isn’t just changing the workflow of software engineering, it’s also changing all the teams around it.

When smaller, AI-enabled teams can get more done, generalists excel. Traditional companies need to pull together people from many specialties — engineering, product management, design, marketing, legal, etc. — to execute projects and create value. This has resulted in large teams of specialists who work together. But if a team of 2 persons is to get work done that require 5 different specialities, then some of those individuals must play roles outside a single speciality. In some small teams, individuals do have deep specializations. For example, one might be a great engineer and another a great PM. But they also understand the other key functions needed to move a project forward, and can jump into thinking through other kinds of problems as needed. Of course, proficiency with AI tools is a big help, since it helps us to think through problems that involve different roles.

Even in a two-person team, to move fast, communication bottlenecks also must be minimized. This is why I value teams that work in the same location. Remote teams can perform well too, but the highest speed is achieved by having everyone in the room, able to communicate instantaneously to solve problems.

This post focuses on AI-native teams with around 2-10 persons, but not everything can be done by a small team. I'll address the coordination of larger teams in the future.

I realize these shifts to job roles are tough to navigate for many people. At the same time, I am encouraged that individuals and small teams who are willing to learn the relevant skills are now able to get far more done than was possible before. This is the golden age of learning and building!

[Original text: https://t.co/1pUxNC5UXk ]

[Daniel Lin @Pofrandom]

I guess we are almost there

[Daniel Lin @Pofrandom]

How soon will agent experience become more important than user experience?

#AX

[Daniel Lin @Pofrandom]

How soon will agent experience become more important than user experience?

#AX