lgiordano_notte

7 ms·

1.
by lgiordano_notte 1y ago
Agree with this. Constraining generation with physics, legality, or even tooling limits turns the model into a search-and-validate engine instead of a word predictor. Closer to program synthesis. The real value is upstream: defining a probl
2.
by lgiordano_notte 1y ago
Shift feels real. LLMs don't replace devs, but they do compress the value curve. The top 10% get even more leverage, and the bottom 50% become harder to justify. What worries me isn't layoffs but that entry-level roles become rare
3.
by lgiordano_notte 1y ago
Cursor’s doc indexing is acc one of the few AI coding features that feels like it saves time. Embedding full doc sites, deduping nav/header junk, then letting me reference @docs inline actually improves context grounding instead of gue
4.
by lgiordano_notte 1y ago
LLMs shift the bottleneck - becomes less about typing code, more about spotting when something’s subtly wrong. Still need real judgment just applied to different layers. The skills that atrophy are surface-level. The deeper ones (debugging,
5.
by lgiordano_notte 1y ago
Don't think the limit is in what LLMs can evaluate - given the right context, they’re good at assessing quality. The problem is what actually gets retrieved and surfaced in the first place. If the upstream search doesn’t rank high-qual
6.
by lgiordano_notte 1y ago
Basically treating extraction as an adaptive loop instead of a static function. If first parse fails or looks incomplete, tweak the prompt, inject more context, or switch strategies. Memory helps carry forward partial wins so you don’t star
7.
by lgiordano_notte 1y ago
Pretty cool. However truly reliable, scalable LLM systems will need structured, modular architectures, not just brute-force long prompts. Think agent architectures with memory, state, and tool abstractions etc...not just bigger and bigger c
8.
by lgiordano_notte 1y ago
Value isn’t just the editor, it’s the workflow. Letting LLMs plan and act across multi-step flows is a hard problem, and Windsurf figured out a dev-focused version of that. Gains to be made in browser automation once you add structure, retr
9.
by lgiordano_notte 1y ago
Really cool direction. The embedding-first + agentic verification pipeline resonates, similar pattern worked well for us in the web interaction space.
10.
by lgiordano_notte 1y ago
Shift isn't just about competitors gaining ground but about users increasingly bypassing traditional search entirely. Between Reddit, Perplexity, ChatGPT, and direct domain knowledge, more queries are being fragmented across tools that
11.
by lgiordano_notte 1y ago
In my experience the key friction point has been schema stability vs input variance. Had better luck treating mapping as a dynamic planning problem with retries and memory.
12.
by lgiordano_notte 1y ago
Agreed. Catching mismatches between doc and implementation is still valuable, just wouldn’t want people to rely on it as a safety net when the docs themselves might be inaccurate/incomplete. As a complement to traditional tests though
13.
by lgiordano_notte 1y ago
A breakdown would be interesting. I can’t give you hard numbers, but in our case scaffolding was most of the work. Getting the model to act reliably meant building structured abstractions, retries, output validation, context tracking, etc.
14.
by lgiordano_notte 1y ago
Curious how you're handling multi-step flows or follow-ups, seems like thats where MCP could really shine especially compared to brittle CLI scripts. We've seen similar wins with browser agents once structured actions and context
15.
by lgiordano_notte 1y ago
LLM-based coding only really works when wrapped in structured prompts, constrained outputs, external checks etc. The systems that work well aren’t just 'LLM take the wheel' architecture, they’re carefully engineered pipelines. Mos
16.
by lgiordano_notte 1y ago
Treating docstrings as the spec and asking an LLM to flag mismatches feels promising in theory but personally I'd b wary of overfitting to underspecified docs. Might be useful as a lint-like signal, but hard to see it replacing real te
17.
by lgiordano_notte 1y ago
If you outsource that to a model, you often end up with words but shallow or no understanding. Writing forces you to clarify your ideas. LLMs substitute genuine thinking with surface-level prose, which might sound alright but often lacks d
18.
by lgiordano_notte 1y ago
Looks really cool. Curious how you're handling action abstraction? We've found that semantically parsing the DOM to extract high-level intents—like "click 'Continue'" instead of 'click div#xyz' helps
19.
by lgiordano_notte 1y ago
In trying to live an intellectually rich life, there's a risk of adding too much noise. Chasing more input, more ideas, more learning. Sometimes less really is more. Depth often comes not from adding, but from subtracting. Clear away t
20.
by lgiordano_notte 1y ago
Makes sense that verbal ability would line up more with success in CS, especially when math scores are already high across the board. A lot of programming leans on language-type skills: reading and understanding code, navigating docs, namin
21.
by lgiordano_notte 1y ago
i wonder how granular the permissions will get though. giving model-level access to something like Gmail sounds powerful, but also like a privacy minefield if not done carefully. curious to see how trust and isolation get handled.
22.
by lgiordano_notte 1y ago
they’re supposed to if the deviation is material. that’s what “pre-announcements” are for. but it’s a gray area unless it’s something truly drastic. a 32% QoQ drop probably qualifies, but companies sometimes gamble that the full context wil
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by lgiordano_notte 1y ago
keybase had promise early on but kinda lost the plot. the vouching system was neat in theory but never really caught on outside a small circle. the crypto stuff definitely didn’t help, and once zoom bought them it was basically a ghost town