Minh Luan

[jordan @jordandotbuilds]

@levelsio Should try serve-sim it essentially streams your simulator so you can access it via a web browser. https://t.co/XMHKp0pzQB this is my setup

[Thariq @trq212]

https://t.co/hPiZr1kG7r

[Aoden Teo @AodenTeoMT]

Today, we’re excited to introduce Miso One, the most emotive voice model in the world.

Miso One is an 8-billion-parameter text-to-speech model for highly expressive speech generation. It emotes like a human and responds faster than a human, with just 110 milliseconds of latency.

We’ve open-sourced the model weights, with API access coming soon.

Hear how Miso One sounds in the thread below.

[Thariq @trq212]

https://t.co/R6exTuF7P8

[Son Luong @sluongng]

Codex just found a “workaround” of not having sudo on my pc…

[GREG ISENBERG @gregisenberg]

My 30+ observations on the greatest opportunities in AI agents right now:

And some ideas that are keeping me up at night.

1. The new buyer on the internet is an AI agent. Imagine billions of new customers showing up with money to spend but they only shop via MCP. That's what's happening. No MCP server means you're invisible to the fastest growing buyer on the internet.

2. Every franchise system in America (30,000+) needs an agent layer and none of them have one. One founder per franchise vertical. That's 30,000 businesses waiting.

3. Everyone said "distribution is the only moat" a year ago. Now I'd add that the only moat is distribution plus memory. The company that has your audience AND your agent's accumulated context is impossible to leave.

4. Consumer mobile is more interesting than it's been since 2012. Apps can finally DO things for you instead of showing you things. The next wave of $100M apps are being built right now.

5. The most interesting startup nobody has built is an agent marketplace where you rent access to someone else's trained agent. A recruiter spent 6 months training a sourcing agent on healthcare hiring. That agent is worth renting to every other healthcare recruiter on earth. The agent itself becomes the product.

6. A sorta strange phenomenon that's happening right now is agents are developing preferences. Give the same agent the same task 100 times and it starts developing patterns in how it approaches it. Nobody is studying this yet. But the agents that develop good patterns are worth more than the ones that don't. That's a new kind of asset.

7. Dead internet theory is about to become dead SaaS theory. Half the apps you use will quietly replace their support team, their onboarding team, and their content team with agents. You won't notice for months. Then you'll realize you haven't talked to a human at that company in a year.

8. The most valuable data in the world right now is sitting in the support tickets of small or mid tier SaaS companies. Every ticket is a customer telling you exactly what to build next. Mine this.

9. The most interesting pricing problem nobody has solved is how do you price a product when your costs change every time OpenAI or Anthropic updates their model pricing? Your margins can swing 40% overnight based on a decision made in San Francisco. The company that builds dynamic pricing infrastructure for agent-based businesses solves a problem every AI company has.

10. The best AI products feel like they're reading your mind. The worst ones feel like filling out a form with extra steps.

11. An interesting arbitrage I've noticed lately is hiring a human VA for $20/hour to supervise an AI agent that does $200/hour work. The human just checks the output.

12. The managed AI agent business is becoming the new agency model. $5k/month per client. You build it, run it, maintain it. The client gets a digital employee they never have to think about. This will be a $50 B+ category.

13. The first "shadow agent" scandals are about to drop. Employees running personal agents on company infrastructure without telling anyone. Using company API keys. Agents accessing internal docs. IT departments have little visibility into this right now. Lots of opportunity to build companies here. Definitely a painkiller not a vitamin type of business.

14. Right now there are probably millions of agents running on autopilot that their creators forgot about. Still burning tokens. Still sending emails. Still scraping websites. Still costing money. The "find and kill your zombie agents" tool is a product that writes itself.

15. Companies are starting to hire based on someone's agent portfolio instead of their resume. "Show me 3 agents you built that are running right now." It's REALLY early but it's starting.

16. Your Slack archive is a product. Every company's internal Slack has thousands of messages explaining how they actually do things. The company that lets you point an agent at your Slack history and auto-generate SOPs and agents from it will be enormous.

17. We're watching the cost of intelligence fall faster than the cost of distribution. Which means distribution is now the expensive thing.

18. The most underrated asset a human can have in 2026: the ability to sit in a room with another human, make eye contact, and have a real conversation. As AI handles more of the transactional stuff, the humans who can do the relational stuff become disproportionately valuable. The soft skills people used to dismiss as fluffy are becoming the hard skills. The hard skills people spent decades acquiring are becoming the soft ones.

19. There are MANY huge companies to be built around the fact that most people's agents are running on their personal laptops which they also use to browse the internet, check email, and download random files. The attack surface is enormous. One compromised Chrome extension and your agent's API keys, customer data, and workflows are exposed.

20. There's a new type of burnout forming that doesn't have a name. It's not from working too hard. It's from context switching between human work and agent work 50 times a day. Reviewing agent output, correcting it, approving it, reviewing again. The mental load of supervising agents is different from the mental load of doing the work yourself. Some founders are telling me they were less tired when they did everything manually because at least the cognitive pattern was consistent.

21. The cheapest form of market research: search "[your industry] spreadsheet template" on Google. Whatever people are tracking manually is your product.

22. Half the YC companies pivoted within 8 weeks of demo day. Not because they failed. Because agents let them test 5 ideas in the time it used to take to test one. The concept of "committing to an idea" is dissolving. Serial pivoting is becoming the default because 1) AI lets you move fast 2) the world is moving fast.

23. The loneliest job in tech right now is being the only person at your company who understands what the agents are doing. You can't explain it to your boss. You can't hand it off to a colleague. If you leave, everything breaks. You've become a single point of failure for an entire automated system. That person needs a title, a team, and a backup plan. Most companies haven't figured this out yet.

24. Your browser history is the most valuable training data you own and you're giving it away for free. Every site you visit, every product you research, every competitor you study, every pricing page you screenshot. That behavioral data, structured and fed to an agent, would make it understand your business better than any onboarding call. The company that lets you turn your browser history into agent context builds something nobody can replicate.

25. Everyone is building AI wrappers. Nobody is building AI unwrappers. The tool that takes an AI-generated document and tells you which parts a human wrote and which parts were generated.

26. Stripe just became the most important company in the agent economy and they barely had to do anything. Every agent that sells something needs Stripe. Every agent that buys something needs Stripe. They're the payment rail for the entire agentic internet by default.

27. The most undervalued API in the world right now is the US Postal Service address verification API. It's practically free. Every local business lead gen agent needs it. Every real estate agent needs it. Every direct mail agent needs it. Boring government infrastructure is quietly becoming the backbone of agent-native businesses.

28. The concept of "business hours" is for humans. Your agent closed a deal in Tokyo at 3am, processed the payment, sent the onboarding email, and updated the CRM before your alarm went off.

29. What happens when agents start recommending other agents? Your research agent finds that a competitor's sales agent is better and suggests you switch. Agent referral networks are forming organically. The first agent affiliate program is probably 6 months away.

30. Cal dotcom closed their source code. That's the canary. When open source companies start closing up, it means agents were cloning their product too easily. Every open source company is quietly asking the same question right now.

31. "AI for pet groomers" sounds like a joke and that's exactly why it will work. 150,000 of them in America. Zero tech. All scheduling by phone or IG DMs. The joke ideas always win.

32. The thing that will seem most obvious in hindsight: we spent 2025-2026 arguing about which model is best while the entire value was in the orchestration layer. The model is the CPU. Nobody buys a computer based on the CPU anymore. They buy it based on what they can do with it. Makes so much sense in hindsight. What else will be obvious in hindsight?

I'll share more notes soon.

I can't sleep with all that's going on. Maybe you too.

What an incredible time to be building.

[Dishant Miyani @dishantwt_]

what a great fckin resource

[Thariq @trq212]

https://t.co/45C3gKydTK

[Arvid Kahl @arvidkahl]

Devs are acting like they didn’t write slop code before AI.

[Nick Van Wiggeren @NickVanWig]

@spacetimedb it can actually. video calls with Postgres

[Mike Bespalov @bbssppllvv]

Quick ASCII wireframe prototyping tool https://t.co/88pTy32Olz

[Dominik Scholz @dom_scholz]

visualizing OpenClaw 🦞🎶

[Mrinal @Hi_Mrinal]

Found a GOATED read on internals of Postgres

https://t.co/VrQhMg2i56

[trish @TrisH0x2A]

bored? try these:

- build your own text editor
https://t.co/1mlTZHYMD5

- make your own operating system
https://t.co/60cAPEFz9C…

- build your own database
https://t.co/qTKFz3V61t

- build your own virtual machine
https://t.co/NJMDjLvOUR

[Phuong Le @func25]

This repository has many Go challenges to help you write idiomatic Go. It looks very promising: https://t.co/7tkTVVetAt

[Jiankui He @Jiankui_He]

Designer babies, super smart or super good-looking, are inevitable.

[Abhishek Singh @0xlelouch_]

Your Golang knowledge, explaining:

Goroutines
Channels
Context cancellation
WaitGroups
Mutex vs RWMutex
Buffered vs unbuffered channels
Worker pools
Select statements
Interfaces
Error wrapping
Generics
net/http internals
gRPC in Go
sync.Pool
io.Reader / io.Writer
Goroutine leaks
Escape analysis
Zero-allocation patterns
Race detector
Memory profiling with pprof

Interview decision:
Sorry, we need someone who actually knows when to use a goroutine.

Knowing Go features ≠ knowing Go engineering.

I’ve seen engineers spin up 100 goroutines for a task that should’ve been a simple for-loop.

They knew what a goroutine is.
They didn’t know why to spawn one.

Understanding fundamentals in Go means asking better questions:

---

1. Do you really need concurrency here?

Just because Go makes concurrency easy doesn't mean everything should be parallelized.

If your job is CPU-bound
— concurrency won’t give you a speedup.

If your job is I/O-light
— concurrency might hurt performance.

Many Go beginners accidentally create spinning goroutine storms for trivial tasks.

---

2. Should this be a goroutine or a worker pool?

Goroutines are cheap — not free.

Thousands? Fine.
Millions? You’ll crash your service.

Worker pools add backpressure, limit concurrency, and prevent cascading failures.

Correct concurrency is about control, not enthusiasm.

---

3. Is a channel even the right abstraction?

Channels are not the default.

Sometimes:

a simple slice is faster
a mutex is simpler
a pipeline is unnecessary
atomic values give lower overhead

Channels introduce synchronization.
Synchronization introduces latency.

Engineers who love channels often write code that’s “clever” instead of correct.

---

4. How will this context propagate?

Most Go outages happen because developers:

forget to cancel contexts
pass background context everywhere
leak goroutines waiting on dead channels
ignore deadlines

If your system doesn’t handle cancellation properly, you don’t have a Go service — you have a time bomb tbh.

---

5. Have you run the race detector, profiler, and escape analyzer?

If you write Go without:

go test -race
go tool pprof
go build -gcflags="-m"

…then you're guessing, not engineering.

Being able to explain:

why a value escapes to the heap
why CPU usage spiked
why memory is ballooning
why goroutines aren’t releasing
is what separates a “Go tutorial graduate” from a real Go engineer.

---

6. Does this service need Go?

Go shines in:

high-throughput APIs
distributed systems
infrastructure tools
networking services
event processing
real-time pipelines

Sometimes Python is better.
Sometimes Rust is better.
Sometimes PostgreSQL stored procedures outperform your Go service entirely.

Knowing Go means knowing when not to use Go.

---

The best Go engineers aren’t the ones who know every feature.

They're the ones who can explain:

why this should NOT be concurrent
why this needs a mutex, not a channel
why this code must avoid allocations
why this API should return errors, not panics

why a simple goroutine is not a “system design pattern”

Anyone can memorize go routines + channels = concurrency.

Not everyone can architect a system that runs clean for months without leaking memory, burning CPU, or failing under load.

That’s the difference between Go as a language and Go as an engineering discipline.

[Vivek Galatage @vivekgalatage]

A relevant blog

https://t.co/ipVFgv63qS

[Mrinal @Hi_Mrinal]

Yoo, finally completed this project ...
https://t.co/ltQ4dQxF80

Wrote a database change capture system in which we capture psql changes using logical replication and using redis for low latency while kafka for durable storage of states

> as we INSERT, UPDATE and DELETE each of them is immediately captured. The change is serialized as Protobuf and pushed to redis streams

> the same events are durably archived to Kafka

> Used Kafka grouped feature, where it has 2 consumers processing events in parallel, while the backup group has 1 consumer. Each group maintains its own offset, allowing different applications to consume the same data at their own pace

> In the django admin board wrote a visualization for showing gRPC calls

Happy Learning :)

[spor @sporadica]

One of a few load-bearing images for me