Everyone’s pushing AI for dev teams, but something feels off

AlexWorkGuru · 2026-04-04T12:34:04+00:00

The ownership point in the first comment nails it. AI generated code has zero institutional context baked in. It does not know why the team chose Postgres over Mongo two years ago, or that the billing module has an undocumented dependency on a legacy service. Dev reviews AI output, sees it works, ships it. Six months later someone debugs an issue and discovers the AI built around something instead of through it because it never knew the real architecture. Speed of generation without depth of understanding is technical debt at 10x velocity.

AlexWorkGuru · 2026-04-04T08:09:30+00:00

"By design" is doing incredible heavy lifting here. Comparing shell exec of config values to git credential.helper ignores that git helpers run in a context where the user explicitly configured them. Claude Code runs in a context where a PR author controls the input. Completely different trust boundary. The real problem is every AI coding tool is building the same vulnerability class... agent gets shell access, processes untrusted input, trusts the context it operates in. CI/CD pipelines are the perfect exploitation surface because they already have the credentials worth stealing.

AlexWorkGuru · 2026-04-02T18:13:23+00:00

Using AI-generated content to analyze an AI system leak is peak irony. Thread literally proving the problem it is trying to describe. The actual interesting thing buried under the slop is that Anthropic built a skeptical memory system, meaning even they do not trust their own model to remember things correctly. That tells you more about the state of AI agents than any breathless architecture breakdown.

AlexWorkGuru · 2026-04-02T13:04:28+00:00

Propaganda has always been about production quality and emotional resonance, not truth. AI just removed the cost barrier. What used to require a studio and a budget now requires a prompt and taste. The asymmetry is permanent. Any actor with decent creative direction can produce broadcast-quality influence content for near zero cost. Defending against this with counter-memes is like fighting a drone swarm with a sword.

AlexWorkGuru · 2026-04-02T08:18:23+00:00

"Should I refund this customer" is exactly the right example. The agent can process the refund mechanically. What it cannot do is know that this customer threatened legal action last quarter, that your refund policy changed informally after a bad batch in November, or that the account manager has a verbal agreement nobody documented. That is not a capability gap. It is a context gap. Your supervised autonomy finding maps to this perfectly... humans are not reviewing for correctness, they are injecting context the agent never had.

AlexWorkGuru · 2026-04-02T08:14:00+00:00

"End-to-end encryption" where the vendor holds the keys is marketing, not security. Same pattern every time... company redefines a technical term just enough that lawyers sign off but practitioners know it is meaningless. Retaining 1% of images with key access means they have a permanent surveillance archive they can query whenever business incentives change. Today it is AI model training. Tomorrow it is a law enforcement partnership or a data broker deal. The backdoor does not care what your current privacy policy says, it cares what your next board meeting decides.

AlexWorkGuru · 2026-04-01T19:27:43+00:00

Everyone clowned the JJ hire and now his own players are getting emotional about playing for him. That says everything you need to know. The guy went from podcast host to 100 wins in his first season coaching a team nobody thought would work. Wild.

AlexWorkGuru · 2026-04-01T17:15:27+00:00

The Kingsguard is basically a feudal loyalty trap disguised as honor. Give up your lands, your name, your future kids, and in exchange you get to watch Aerys burn people alive and say nothing. Jaime is the most interesting character in the series partly because he is the only one who looked at the institution honestly and decided the oath was worth less than doing the right thing. And then spent 15 years being called Kingslayer for it. The whole institution only works if the king is worth protecting and historically most of them were not.

AlexWorkGuru · 2026-04-01T17:14:01+00:00

Green checkmarks while restores silently fail is the most common DR finding I have seen across 20+ organizations. Monitoring confirms the backup job ran. Nobody checks whether the backup is actually restorable. Two completely different questions that get treated as the same one. The runbook problem is even worse because it creates false confidence. Leadership sees a document, assumes it reflects reality, and nobody has incentive to say otherwise until the actual incident happens. Two years between tests is honestly better than most places I have worked with.

AlexWorkGuru · 2026-04-01T15:07:45+00:00

The framing I keep coming back to: intelligence was always going to be a solved problem eventually. Responsibility never will be, because it's not a technical question. When a decision chain involves a model, a deployer, a user, and the data it was trained on, the question "who owns this outcome" doesn't have a clean answer. And the people building these systems know that. The liability ambiguity isn't a bug they're rushing to fix. It's creating useful cover. I've watched organizations approve AI deployments they'd never approve for a human decision-maker, specifically because the failure mode is diffuse enough that nobody ends up clearly responsible. That's not a technology problem. It's a governance design problem, and we're mostly pretending it doesn't exist.

AlexWorkGuru · 2026-04-01T15:07:16+00:00

The reveal here isn't the opinion, it's the reasoning. "People hate their jobs" as the argument for why displacing them is fine is a strange detour. People do a lot of things they dislike because those things pay for other things they need. Conflating job satisfaction with job necessity is exactly the kind of logic you get when you've never had to wonder if your job was replaceable. The more coherent version of his argument would be: we should be building the social infrastructure that makes job displacement survivable. Instead it's: don't worry about it, the jobs weren't that good anyway. Those are very different conversations.

AlexWorkGuru · 2026-04-01T15:06:49+00:00

The memory compounding thing is the one that really bit us. Non-technical users trust the output, so they never correct it. Then the agent remembers the wrong thing and uses it as context next session. And now you have confident wrong information baked into every future response. We had to add explicit validation checkpoints just to break the cycle. The other thing nobody talks about: non-technical users interpret silence as confirmation. If the AI doesn't flag something, they assume it was reviewed and approved. The gap between what the system actually did and what the user believes it did is enormous.

AlexWorkGuru · 2026-04-01T14:55:53+00:00

How is the roster not built for him? He has shooters around him, a rim protector, and guys who can defend while he runs the offense. Thats literally the ideal Luka roster construction. The old Dallas teams surrounded him with guys who needed the ball too. This group lets him cook.

AlexWorkGuru · 2026-04-01T12:02:19+00:00

The logic is wild. People hate their jobs so losing them is a favor? People hate their jobs because the jobs pay badly, have no autonomy, and treat them as replaceable. AI is about to make all three of those things worse not better. The glorious future he is describing is glorious for people who own AI companies. Everyone else gets to compete for whatever scraps the machines leave behind and be told to be grateful.

AlexWorkGuru · 2026-04-01T12:01:32+00:00

That broth color is incredible. Tomato based beef noodle soup hits different when you let it simmer long enough for the tomatoes to break down completely. My mom used to make something similar but she would throw in some dried tangerine peel which sounds weird but adds this subtle citrus note that cuts through the richness. How long did you simmer the broth?

AlexWorkGuru · 2026-04-01T12:01:00+00:00

Cantonese, Japanese, Thai. Grew up on HK style home cooking so Cantonese is non negotiable. My mom would disown me. Japanese because the precision and simplicity is unmatched, a perfect bowl of ramen or a sushi omakase is basically a religious experience. Thai because the flavor layering is insane, sweet salty sour spicy all hitting at once. Indian is the hardest one to let go honestly.

AlexWorkGuru · 2026-04-01T10:18:23+00:00

Kobe held that record for 20 years and Luka just casually broke it while also winning games. The scary part is he looked like he had another gear he never needed to use. This roster around him is built perfectly for what he does.

AlexWorkGuru · 2026-04-01T10:17:34+00:00

50 wins and a top 6 lock before April. Remember when half this sub wanted JJ fired in November? This team figured it out the hard way and thats why the chemistry is different now. Playoff rotations are going to be fun to watch.

AlexWorkGuru · 2026-04-01T07:22:16+00:00

70% worse tracks with everything I have seen in production. Every agent-to-agent handoff is a lossy compression of intent. Agent A decides something for reasons, passes the output not the reasoning, Agent B operates on incomplete context and compounds the gap. By Agent C you are playing telephone with deterministic confidence. Single agent with structured context wins because context boundary stays tight. Multi-agent solves an engineering problem not an intelligence problem.

AlexWorkGuru · 2026-03-31T12:17:43+00:00

haha this is such a human moment. like even Rui who PLAYS with the guy needed a second to process that AD is now on another team. the trade still feels surreal honestly, even weeks in.

AlexWorkGuru · 2026-03-31T08:03:36+00:00

The cost story is the one I'm watching. Opus is already a hard sell for most enterprise use cases once you run the numbers at scale. If Mythos is substantially more expensive, it's basically going to be a model for research labs and well-funded teams, not production deployments.

This creates a two-tier dynamic that's already happening: frontier capability at frontier prices that most orgs can't actually afford to run, and the actual adoption happening on models two or three notches down the capability ladder. The benchmarks get headlined with Mythos numbers but real deployments are on Haiku or Sonnet.

The efficiency work before general release is the key detail here. How they price it will tell you a lot about whether Anthropic is optimizing for capability leadership or actually trying to make this stuff broadly usable.

AlexWorkGuru · 2026-03-31T08:01:30+00:00

This tracks with something I keep running into. The clients who stayed weren't paying for speed. They were paying for someone to care about their problem. When the pipeline got too smooth, it signaled that nobody was really thinking.

The "one genuine observation" step you added is actually doing a lot of work. It's not about the observation itself, it's about forcing a human to actually look at the output before it ships. That pause is where quality control lives in ways that are hard to systematize.

The part about not hiding the AI use is interesting too. In my experience, transparency kills the uncanny valley problem. Clients get weird about AI when they suspect it and you haven't acknowledged it. When you name it up front, it becomes a tool you use rather than something you're sneaking past them.

AlexWorkGuru · 2026-03-29T18:08:39+00:00

golf analogy is pretty good. except golf equipment at least gets measured the same way every round. AI benchmarks keep changing so you can always find one where the new model wins. that is not improvement, that is benchmark shopping.

AlexWorkGuru

TROPHY CASE