The Prime Guide For Next-Gen Developer

A Guide to Grow as a Competent Software Developer written by @TheAnkurTyagi. Buy here:

[The Prime Guide For Next-Gen Developer @PrimeBookForDev]

Hi 👋 Followers.

Are you guys ready for Pre-Sale.

@TheAnkurTyagi will launch 🚀 me today evening IST hours.

Stay Tuned!!

[Robert Scoble @Scobleizer]

Developers and builders!

Everyone is shipping agents and nobody has a clean system for seeing what they actually do in production.

@trylatitude just open sourced their monitoring platform with the see catch fix flow built in. You get clustered failure patterns, auto evals, and direct editor integration through MCP.

Self hostable or free tier. No paywall on core features because it is MIT licensed.

Try it: https://t.co/TvYYQ7DHIO

[Dhaval Makwana @heyDhavall]

The thing that clicked for me with Latitude:

Your agent is already talking to thousands of users.

It's collecting product feedback, feature requests, complaints, and success stories every day.

Most teams never look at any of it.

That's a much bigger problem than a lack of data.

[Pradeep Pandey @Div_pradeep]

Every AI team is obsessed with model quality.

Very few are paying attention to user frustration, failed expectations, and churn signals.

That’s where the real insights are.

Interesting approach from @trylatitude.

[The AI Colony @TheAIColony]

Most teams have no idea what their agent has been doing.

Every session it runs is a black box.
Every silent failure disappears into logs nobody reads.

Latitude watches all of it, clusters the failures into tracked issues, and flags exactly what needs fixing before users ever notice.

[Csaba Kissi @csaba_kissi]

AI agents are basically sitting on a goldmine of customer insights.

Every question, complaint, objection, and feature request is already there.

Most companies just don’t capture it properly yet.

[FELIX @FellMentKE]

Latitude is solving a critical problem in the AI space by turning agent conversations into actionable data.

Most observability tools miss the silent failures and unmet user expectations that lead to churn.

@trylatitude provides the visibility needed to understand and fix these issues in real time.

[Gerard Clos @geclos]

The new Latitude is now open to all users.

Open source. MIT licensed.

Check it out and lmk what you think.

[George Amit | AI tools and Content Creator @Amitxai]

Companies obsess over dashboards.

The real insights are hiding somewhere else:

AI conversations.

This week, we discovered that our agent had already collected months of customer feedback without anyone realizing it.

Feature requests.
Pain points.
Buying signals.
Success stories.

Thousands of conversations.

Zero surveys required.

Your AI agent isn’t just doing work.

It’s generating customer intelligence at scale.

[Pratham @Prathkum]

You are paying six figures for a pretty log viewer when your agent’s own conversations are the cheapest source of truth you have.

Latitude is exactly that.

[elvis @omarsar0]

Very cool to see more focus on agent observability tools.

I pointed Latitude at my Claude Code setup and immediately saw which tasks were actually eating my token budget.

It's a great way to:

> See what your agent does
> Catch the failures that keep repeating, with the frequency and reason
> Fix them without leaving your editor

Open source and MIT licensed!

Try it free at https://t.co/awVYJqHtUW

[Rajesh Kumar @therjrajesh]

One realization changed how I think about AI agents:

They’re not just automating support.

They’re becoming the largest source of customer research inside a company.

Thousands of conversations happen every week.

Most teams archive them.

The smart teams analyze them.

Every “thank you.”
Every frustration.
Every feature request.

That’s product research, customer success, and market intelligence rolled into one.

The insights are already there. The challenge is finding them.

[Shraddha Bharuka @BharukaShraddha]

AI observability shouldn't stop at latency, token usage, and costs.

The real question is:
👉 Did the user get what they wanted?
👉 Were they frustrated?
👉 Are they likely to come back?

That's the data that actually moves the business.

[Aastha @aastha_mhaske]

Wild that we'll deploy agents to thousands of users and still rely on manual user interviews to understand what happened.

Your conversations already contain the answers.

See what users do.
Catch what breaks.
Fix it from your editor.

That's a much better loop.

[Chubby♨️ @kimmonismus]

Been waiting for someone in agent observability to do this properly. Latitude doesn't just hand you traces.

It collapses repeated failures into one issue with frequency and reason, and lets you search production conversations in plain English.

Open source, MIT licensed, self-hostable. Rare in this category. (and you know that i love open source)

If you run agents in prod, the Claude Code token telemetry is the part I'd try first.

[KP @thisiskp_]

Isn’t this true?

Your AI agent hears every complaint, confusion, feature request, and churn signal FIRST.

Every company is sitting on a goldmine of customer intelligence but most never see it.

The winners won't build better agents.

They'll learn faster from the conversations their agents already have.

That’s what @trylatitude is betting on, check out their launch 👇👇

[Krishna Agrawal @Krishnasagrawal]

How did this not exist already?

Your AI agent talks to every customer, sees every edge case, and hears every complaint.

Yet most teams never read that data back.

Latitude flips that:
See → Catch → Fix.
Open source and MIT licensed.

[Rohan Paul @rohanpaul_ai]

Agents token burn needs more visibility.

Latitude just launched an open-source, MIT licensed monitoring platform that turns AI agent conversations into production debugging data.

Most agents already talk to more users than any teammate, but those chats usually sit as raw logs, so teams miss frustration, unmet requests, repeated failures, and churn signals.

Latitude organizes that mess into a see, catch, fix loop: see sessions, users, tools, cost, latency, and behaviors; catch repeated failures through Signals; fix them from your editor through MCP.

The product is built for production agents, not just model calls, because agent quality depends on tool use, user intent, retries, cost, latency, and the gap between what the user expected and what the system did.

[Harnoor Singh @iHarnoorSingh]

I never leave the CLI even for dashboards computer use makes it and recently realized i can measure all my user data but have zero visibility into my agents’ actual loops: tool calls, reasoning steps, silent failures, nothing

this open source repo changes that. @trylatitude gives you full session traces, automatic detection of frustration/escalation/churn signals, cost + latency per loop, and turns real prod conversations into eval datasets for regression testing

setup is literally one prompt in my coding agent (or npx in terminal) + MCP so i can inspect/fix issues without leaving the flow. finally something that actually fits a terminal-first workflow 🔥

MIT licensed: https://t.co/XcKmXEsGJi

[Minh-Phuc Tran @phuctm97]

This is the new analytics software.

Most software’s data in the future will be chat history with AI agents, instead clicking pattern / UI heatmap.

And it’s MIT licensed! 🤯

Gonna give it a try for my new agent orchestration platform. 🤫

[shirish @shiri_shh]

reading your own agent's conversations is about to be the highest-leverage move a startup can make.

the data's already in your logs.

you're either mining it or you're guessing.