Nikolas Bakalis

Senior Software Engineer | Applied AI & Backend

[Nikolas Bakalis @Nikolas_Bakalis]

@aaryanaint The people who can build and ship a startup in a weekend usually understand the fundamentals too. AI amplifies skill; it doesn’t create it.

[Lenny Rachitsky @lennysan]

My biggest takeaways from @Netflix's Chief Product and Technology Officer Elizabeth Stone:

1. Elizabeth believes that “systems thinking” is becoming the most important skill in the AI era. In engineering and product, this means people who can see across business domains and build the common capabilities that let many teams move quickly. In design, it means experience designers who create templates and design systems so that non-designers can ship work that stays coherent and on-brand. The underlying driver is velocity: when more people are doing more types of work at higher speed, you need to be good at building common scaffolding.

2. Systems thinking is learnable: zoom out one level from your specific problem. Given a task, step back one click—what bigger problem does this serve the business, will it scale across the product surface areas, should it become a platform capability? The companion habit: do your job in a way that helps your manager do theirs. This will force you to think about how all the pieces fit together.

3. Expect a storming phase before a forming phase. The role confusion people feel right now (“What is my job anymore?”) is the predictable middle of any transformative technology. Elizabeth’s advice: focus on high-quality source-of-truth data, guardrails on what ships, and constant internal reinforcement that humans own what they create.

4. The top AI labs converged on Netflix’s culture. High agency, high talent density, top-of-market pay, bottom-up thinking, fast experiments—the traits Lenny hears constantly from AI labs were in Netflix’s early culture deck. Elizabeth’s explanation: excellence comes from hiring exceptional people, trusting them to do great work, and holding them accountable.

5. Netflix’s culture is centered around building “excellence as an operating system.” High talent density, radical transparency, context not control, and the keeper’s test. These work together to create an environment of trust and accountability, without bureaucracy. But it’s also uncomfortable. It requires tolerating people making decisions you’d make differently, resisting the reflex to add process when things go wrong, and letting people carry the weight of their own choices. Elizabeth describes the hardest part as “being comfortable in that discomfort.”

6. The keeper’s test is as much about recognizing great people as it is about removing the wrong ones. The test—“If this person told me they were leaving, would I fight to keep them?”—is often cited in its difficult form: the moment you realize someone isn’t the right fit. But Elizabeth uses it predominantly as an entry point for honest performance conversations that are deeply positive. Most of the time the answer is “I would fight so hard to keep you,” which creates the opening to articulate strengths, discuss impact, and name what’s working. Good feedback hygiene needs a forcing function; the keeper’s test provides one.

7. Specialization is trending down—adaptable generalists are trending up. We’re shifting away from narrow stack-layer specialists (pure frontend, pure backend) toward people who can navigate fluidly across layers. The same logic applies to business domain knowledge: the mindset of “I’m a payments expert, full stop” is less valuable than “I know payments well enough and I’m willing to imagine what the future version of this looks like.” The meta-skill is learning to learn, not locking into a single lane.

8. Netflix’s approach to AI fluency is a universal principle, not a level-specific expectation. Rather than rewriting career ladders to specify what AI competence looks like at each level, Netflix added a single aspiration across all roles and levels: AI fluency. What fluency means varies by function and seniority, but the non-negotiable minimum is the same everywhere—an open-minded, experimental mindset, genuine curiosity, and comfort with ambiguity.

[Andy Fang @andyfang]

Today we're opening up the DoorDash CLI in limited beta.

`dd-cli` lets you order DoorDash directly from your agent: search stores, find the best deals, check out, and more.

Early access for US/Canadian macOS developers by waitlist. Excited to see what folks build!

[Nikolas Bakalis @Nikolas_Bakalis]

@Taniyatweets_ My main reason is because docker works so much better on MacBooks.

[Nikolas Bakalis @Nikolas_Bakalis]

@bryan_johnson It never matters what you are doing.

It only matters if you do it (whatever it is) or not.

[Neo Kim @systemdesignone]

How Uber computes ETA at scale.
(explained in 2 mins or less):

[Peter Steinberger 🦞 @steipete]

Let me verify a couple of load-bearing facts before I give you the architecture assessment.

[Nikolas Bakalis @Nikolas_Bakalis]

@rackSpreader1 Math is useful in software engineering, but usually less important than problem-solving, logic, and writing clean code. Most SWEs rarely use advanced math daily, unless they work in ML, graphics, cryptography, quantitative finance, or similar fields.

[Nikolas Bakalis @Nikolas_Bakalis]

@chaosengineerr It’s not really about refusing to use AI.
It’s about how willing you are to adapt to change and combine new tools with the experience you already have.
Senior engineers are often slower to change, especially when it challenges how they’re used to working.

[Paul Graham @paulg]

If you want to start a startup, don't learn "entrepreneurship." Learn how to build things. The hard part of startups is not "entrepreneurship" but product: to know what to build, and to be able to build it.

[Nikolas Bakalis @Nikolas_Bakalis]

@GoogleAIStudio Adding to my codex/claude wrapper to improve code quality for my team and anyone else.
https://t.co/YOC8H6tFZX

[Nikolas Bakalis @Nikolas_Bakalis]

@DevBegie Do not rush on AI.
It is more than what most users understand. We are thousands of years after fire was created and people still burn things down with it.

Learn the fundamentals first. Then use AI to become faster, not dependent.

[Nikolas Bakalis @Nikolas_Bakalis]

@rezoundous Curious, why do you use both for your own workflow? Are you comparing them, benchmarking them, or do they serve different purposes for you?

[Nikolas Bakalis @Nikolas_Bakalis]

@akramcodez https://t.co/hpksyyCPqR is what I am using

[Nikolas Bakalis @Nikolas_Bakalis]

@Polymarket Good

[Nikolas Bakalis @Nikolas_Bakalis]

@Zinny_Edmund Knowing languages helps you pick the right tool for each case. If performance matters most, a lower-level language like C++ may be the right choice. If speed of development matters more, Python or higher-level languages may be better. Different problems need different trade-offs.

[Nikolas Bakalis @Nikolas_Bakalis]

@CodeByNZ Codex, Claudes biggest issue is that it doesn't know when to stop hallucinating and ask clarifying questions.

[Nikolas Bakalis @Nikolas_Bakalis]

@thdxr You need code quality when the product must scale. You also need fast, scrappy code when speed matters more than perfection. Most importantly, you need open discussion to understand which approach the situation calls for. That’s where many companies fail.

[Burak @xburak]

Unpopular opinion:

Vibe coding only works if you already know how to code.

[Nikolas Bakalis @Nikolas_Bakalis]

@systemdesignone Adding to my codex/claude wrapper to improve code quality for my team and anyone else.
https://t.co/YOC8H6tFZX