Alejandro López

Senior Staff Software Engineer at @factoriales. Previously at @ZARA. Open Source Contributor. JS?

[Satya Nadella @satyanadella]

https://t.co/vLmiBKTtX3

[Cursor @cursor_ai]

Five themes emerged in our research: developer acceleration, the economics of intelligence, the power user gap, the rise of context, and the shift to automation.

Read the full report here: https://t.co/7ud79SzX9p

[Tom Dörr @tom_doerr]

500-hour AI infrastructure engineering curriculum

https://t.co/wVx160z46c

[Zeb Evans @DJ_CURFEW]

Today we reduced headcount by 22%. The business is the strongest it's ever been. So I think it's important to be direct about what I'm seeing and why.

First, I made this decision and I own it. I did it because the way to operate at the highest level of productivity is changing, and to win the future, ClickUp needs to change with it.

Second, this wasn't about cutting costs. Most savings from this change will flow directly back into the people who stay. We'll be introducing million-dollar salary bands. If you create outsized impact using AI, you'll be paid outside of traditional bands.

Most importantly, I have the deepest gratitude for those affected. We're doing this from a position of strength specifically so we can take care of people properly. Everyone affected receives a package aimed at honoring their contributions and easing the transition.

I only see two options: wait for this to play out gradually in the market or be honest about what I'm seeing and act proactively.

THE 100X ORGANIZATION
The primary change is that we're restructuring around what I call 100x org. The goal is 100x output. The roles required to build at the highest level are fundamentally different than they were a year ago.

Incremental improvements to existing systems won't get us there. We need new ones. That means creating enough disruption to rebuild rather than iterate on what's already broken.

The common narrative is that AI makes everyone more productive. It doesn't. Many of the workflows of today, if left unchanged, create bottlenecks in AI systems.

These roles will evolve. But waiting for that to happen naturally means falling behind now.

The 100x org is actually heavily dependent on people - infinitely more than today. This is only possible with 10x people that have embraced and adopted new ways of working.

THE BUILDERS, AGENT MANAGERS, AND FRONT-LINERS

— THE BUILDERS: 10X ENGINEERS
I don't think most companies have internalized what's actually happening with AI in engineering. The common narrative is that AI makes all engineers more productive. That may be true in isolation, but at an organization level - that is the farthest thing from reality.

Here's what we've validated recently at ClickUp: the great engineers, the ones who can orchestrate, architect, and review, are becoming 100x engineers. They're not writing code. They're directing agents that write code. The skill is judgment.

AI makes the best engineers wildly more productive, and everyone else using AI slows these engineers down.
Think about it - the bottlenecks are (1) orchestration - telling AI what to do, and (2) reviewing - what AI did. Everything is leapfrogged and no longer needed.
So who do you want orchestrating and reviewing code?

And how do you want your best engineers to spend their time?

If your best engineers are spending time reviewing other people's code, then this is inherently an inefficient bottleneck. These engineers can review their agent's code much faster than reviewing human code.

The new world is about enabling your 10x engineers to become 100x.

The wrong strategy is to push every engineer to use infinite tokens. Companies doing this are celebrating 500% more pull requests. But customer outcomes don't match the volume of code being generated.

I call this the great reckoning of AI coding, and every company will face this soon if not already.

More code is just another bottleneck to the best engineers, and ultimately to your company's impact as well.

— THE BUILDERS: 10X PRODUCT MANAGERS
Product management and design roles are merging.

Designers that have customer focus, become more like product managers.

And product managers that have intuition for UX become more like designers.

The bottleneck of user research is gone. It takes us just one mention of an agent to kickoff research and analyze results.

The bottleneck of product <> design iteration is also gone. The product builder iterates on their own, along with agents and skills that ensure alignment with quality and strategy.

Also controversial today - I believe that the wrong strategy is to have your PMs shipping code - that just introduces another bottleneck that the best engineers will waste their time on.

To be clear, PMs should be coding but they should do this in a playground to iterate, validate, and scope. That code should not go to production.

Everything outside of managing systems, orchestrating AI, and reviewing output becomes a bottleneck.
That's why the other roles that are critical along with these are the systems managers (to reduce bottlenecks) along with a bottleneck you can't replace - customer meeting time.

— THE SYSTEM MANAGERS
Ironically, the people that automate their jobs with AI will always have a job. They become owners of the AI systems - agent managers. We have many examples of these people at ClickUp.

The underlying systems in which we operate are absolutely critical to get right. I think most companies are delusional to think they can iterate on existing systems and compete in this new world.

You must create enough disruption so that old systems are deprecated entirely. If there's any definition for 'AI native' that's what it is.

— THE FRONT-LINERS
In a world that will become saturated with AI communication, the human touch will matter more than anything to customers.

This is a bottleneck that you shouldn't replace - even when agents are high enough quality to do video meetings.

One-on-one meeting time with customers is something that shouldn't be automated. The systems around the meetings should be - so that front-liners spend nearly 100% of their time with customers.

REWARDING 100X IMPACT
In a world where companies are able to do so much more with less, where does that excess money go?
In our case, much of the savings in this new operating model will flow directly back to those that enabled it.

We must reward people that create productivity accordingly. This aligns incentives on both sides. Plus, in a world where your best people create 100x impact, you can't afford to lose them.

You should aim to retain these employees for decades. The context they have and their ability to efficiently orchestrate and review will be nearly impossible to replace.

Compensation bands of today should be thrown out the door. We're introducing $1 million cash/year salary bands with a path available to nearly everyone in the company if they produce 100x impact by creating or managing AI systems.

THE FUTURE
Nearly every company will make changes like these. The ones that do it proactively will define what comes next.

The future is not fewer people. It's different work, new roles, and better rewards for those who embrace it. We're already seeing entirely new roles emerge, like Agent Managers, that didn't exist a year ago.

ClickUp is positioning to lead this shift, not just internally, but for our customers too. I've never been more certain about where we're headed.

[Guillermo Rauch @rauchg]

If you become exceptional at managing agents, but are also exceptional in your understanding of the fundamentals, you will be unstoppable.

We all prefer to work with masters of their craft. What’s new: you can’t afford to miss out on the amplification agents have on your output

[Mitchell Hashimoto @mitchellh]

Ghostty is leaving GitHub. I'm GitHub user 1299, joined Feb 2008. I've visited GitHub almost every single day for over 18 years. It's never been a question for me where I'd put my projects: always GitHub. I'm super sad to say this, but its time to go. https://t.co/DQDemHdytV

[Warp @warpdotdev]

Warp is now open-source.

[Warp @warpdotdev]

Why devs love @opencode:

&gt; fully open source harness + privacy-first
&gt; butter-smooth terminal TUI
&gt; bring your own model: Claude, GPT, Kimi, etc.
&gt; LSP-smart, multi-session agents, git-native

What’s your favorite feature of OpenCode?

[Miguel Ángel Pastor @miguelinlas3]

Design and Implementation of DuckDB Internals https://t.co/iCB56Np0Bu

[Ethan Evans @EthanEvansVP]

I became an Amazon Tech VP because I made decisions that turned into money. Coding skills, a "hard skill" did not matter despite the "tech" in my title. All your hard skills will be irrelevant soon, so learn from my experience:

[Abhishek Singh @0xlelouch_]

CTO: We lost our strongest backend engineer today.

Founder: The one handling infra and outages?

CTO: Yes.

Founder: Did a bigger company hire him?

CTO: No.

Founder: Then why quit?

CTO: He said he was exhausted.

Founder: From the workload?

CTO: Not exactly. From watching the same database bottleneck, same queue lag, same deployment mistakes come back every month.

Founder: That happens in fast moving teams.

CTO: He agreed. What he could not accept was that every fix was temporary because nobody wanted to slow down and clean the system properly.

Founder: We had deadlines.

CTO: He had standards.

Founder: So he left because the work was hard?

CTO: No. He left because he was not doing engineering anymore. He was just containing damage.

The best engineers do not hate hard problems.

They hate preventable problems that management keeps normalizing.

[Warp @warpdotdev]

You can now review the code written by Claude Code, Codex, OpenCode, and more directly in Warp, and send inline comments straight to the agent.

It's like a PR review without having to leave your terminal. Try it out!

[Ben Dicken @BenjDicken]

90% of building fast applications is holding the database correctly.

The other 10% is killing N+1s.

[Thariq @trq212]

Using Skills well is a skill issue.

I didn't quite realize how much until I wrote this, the best can completely transform how your team works.

[Harrison Chase @hwchase17]

a lot of engineering orgs (Stripe, Ramp, Coinbase) are building internal cloud coding agents

we're releasing a fully OSS one today - every company should have the power of cloud agents at their fingertips

[Michael Truell @mntruell]

https://t.co/jZKOk8RAsV

[Ben Dicken @BenjDicken]

If databases fascinate you like they do me, this article's for you!

Every time you interact with a website, database transactions are keeping your data consistent, safe, and isolated.

I wrote an interactive guide to how they work ⬇️

[Abhishek Singh @0xlelouch_]

A Staff Engineer isn’t an “extra strong Senior.” It’s a fundamentally different job.

At the Staff level, your primary output is decision quality, not code velocity. If the company only needed more throughput, they’d scale headcount or buy tools. They bring in Staff Engineers when the cost of a bad decision exceeds the cost of an extra hire.

On paper, a team with multiple Staff Engineers looks inefficient. In practice, that leverage is what prevents the organization from collapsing under its own complexity. As systems grow, the biggest risk is no longer “can we build this?” but “are we building the right thing, the right way, at the right time?”

A Staff Engineer’s real work starts when things are unclear:
– Requirements are fuzzy
– Trade-offs are uncomfortable
– Deadlines conflict with correctness
– Short-term wins threaten long-term health

Your job is to slow the team down just enough to ask the questions others don’t have time—or permission—to ask.

Where many new Staff Engineers go wrong is mistaking authority for impact. Complaining about legacy systems, proposing rewrites, or benchmarking against famous companies is easy. It feels productive. But without deep context, it’s just noise. Most legacy exists because it once solved a real problem under real constraints.

A good Staff Engineer does the opposite: They learn why the system is the way it is. They understand the business pressures shaping technical decisions. They identify which constraints are real—and which ones can be challenged.

The most valuable thing you do is not writing elegant code. It’s preventing irreversible mistakes:
– Choosing the wrong abstraction too early
– Over-engineering before scale exists
– Under-engineering systems that must scale
– Creating coupling that blocks future product moves

You guide product and engineering leadership through trade-offs they don’t have the technical depth to fully see. You translate long-term technical risk into business language. You help the team avoid decisions that feel good this quarter but cripple the roadmap next year.

Senior Engineers ship features. Staff Engineers protect the system and the business that depends on it.

If you measure your impact by commits or tickets closed, you’ll miss the point. If nothing explodes on your watch, that’s usually success.

[Alejandro López @aristalex]

@BraydenWilmoth Origin latency metrics with percentiles in Analytics &amp; logs &gt; HTTP Traffic would be awesome!

[Arpit Bhayani @arpit_bhayani]

Yes, CDN improves latencies by caching things closer to the users, but here's an interesting optimization they do to optimize on latencies...

TCP suffers from slow starts, i.e., when a new TCP connection is established, it doesn't immediately operate at full bandwidth. Instead, it begins conservatively with fewer segments and doubles them each round-trip time until it detects congestion.

This ramp-up can take several iterations to achieve optimal throughput, which is problematic for latency-sensitive applications. This is a big problem for CDN, because even a few additional round-trips for a massive scale costs a lot.

CDNs solve this by maintaining persistent connection pools to origin servers. Rather than establishing fresh connections for each user request, they keep a pool of long-lived connections alive between edge nodes and origins.

The clever part is pre-warming during low-traffic periods.

CDNs periodically send small amounts of data (like health checks or cache validation requests) over these idle connections. This keeps the TCP congestion window at max and prevents it from shrinking due to inactivity.

Here's a simple calculation to quantify the impact.

By keeping the congestion window pre-warmed, it is operating at 64KB instead of 4KB. This eliminates the 3-7 round-trip ramp-up delay that would otherwise occur.

For a connection with 50ms round-trip time, this saves 150-350ms of latency, and that's pretty significant for something that operates at web scale, literally.

Hope you found this interesting, and like always, keep digging deeper.