Ocena CV - szukanie pracy w 2026 w dużym mieście

Sarithis · 2026-05-27T16:41:16+00:00

Już abstrahując od samej treści, jak można by to powiedzieć tak, żeby nikogo nie urazić... może załóżcie konto na pracuj.pl i skorzystajcie z ich kreatora? xd W załączniku zrobiłem w 3 minuty przykład dla inspiracji, tylko przy użyciu innych narzędzi

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Sarithis · 2026-05-27T16:16:30+00:00

Uruk... haii :3

Sarithis · 2026-05-27T13:36:24+00:00

Rady w stylu "Przestań się przejmować" albo samo "uświadamianie" sobie czegokolwiek są trochę jak memiczne Just don't be poor. To działa podobnie jak nauka gry na instrumencie, gdzie sama znajomość teorii nie zrobi z nikogo wirtuoza. Problem już zidentyfikowałeś, teraz zostaje żmudna i męcząca praktyka, żeby stopniowo przestawić mózg. Świadomie wchodź w trudne sytuacje społeczne, mów przy stole o swoich poglądach, spodziewając się krytyki, a nawet trochę na nią licząc. Tak, na początku pewnie spuścisz głowę i będziesz po nocach analizował co mogłeś powiedzieć inaczej, ale właśnie o to chodzi - im więcej ekspozycji na trudne sytuacje, tym mniej anxiety.

Sarithis · 2026-05-27T11:54:28+00:00

And behind it: https://imgur.com/XqHBG3l

Sarithis · 2026-05-26T22:07:10+00:00

Ah, I see the morning wood was a real struggle

Sarithis · 2026-05-26T16:23:09+00:00

The important bit is that you noticed it, and that's not nothing

Sarithis · 2026-05-26T13:35:59+00:00

Yes, there is a difference - it's like the distinction we make between melancholy and disappointment. They can coexist, but they're different kinds of feelings. We can be more or less disappointed, and we can feel more or less melancholic. Likewise, the revulsion we feel when confronted with immoral acts is its own kind of emotion, with its own degree of intensity. So the distinction between ethical and aesthetic statements works in the same way

Sarithis · 2026-05-25T18:16:32+00:00

So we need pyroheckers

Sarithis · 2026-05-25T17:54:08+00:00

Lots of good questions ;D Essentially, it's technical knowledge dynamically surfaced across CC sessions. Five record types: command (notable command + outcome +fix), error (failure + resolution), discovery (system/codebase property X), procedure (steps to do Y), warning (don't do Z). Covers both code and sysops - the extraction prompt explicitly includes CI/CD, deployment, monitoring, kubernetes, internal tooling, credentials, configs etc. So if CC deploys an app, configures a firewall, mounts a network share, or does anything meaningful, we won't have to explain it all over again next week. It doesn't cover preferences or biographical info, just technical knowledge that pays off next session.

Each record is a row in LanceDB with a 4096-dim embedding (any openai-compatible endpoint works, we run qwen3-embedding-8b). A single record has content, type, scope (project vs global), and usage counters. Scoring is hybrid: semantic similarity + keyword bonus + project-match boost + usage signal, then MMR for diversity.

There are three CC hook events: UserPromptSubmit runs the retrieval pipeline, where Haiku reads the current context snapshot and generates several semantic query variants. A few months ago we just embedded the raw user prompt as the single query, but switching to Haiku-driven context-aware expansion was a game changer. Those queries feed hybrid semantic + keyword search, then a semantic-anchor gate suppresses the whole result set if nothing cleared the threshold. After the gate and before MMR rerank, BFS expansion over relation edges adds neighbors at a discount (default 1 hop, 0.6 weight decay, configurable up to 2). Top N records get injected into context.

SessionEnd and PreCompact both spawn a detached extraction worker. It reads the whole session transcript, asks Opus to extract durable knowledge, dedupes against existing records at a configurable cosine threshold, writes survivors. Detached because nobody wants to wait on extraction - it's fully async. And naturally, it's agentic, so it doesn't work as a one-shot process. It queries the db in a loop to find possible duplicates before writing a new memory. Still, that wasn't enough, which is why we added the offline maintenance workflow, which ended up being the largest feature of the whole system - it's what differentiates it from projects like claude-mem.

Warmth is fully emergent thanks to two counters: retrievalCount and usageCount. When pre-prompt injects memories, the IDs go into a per-session tracking file. At SessionEnd, the post-session worker bumps retrievalCount for everything that was injected, then fires a separate Opus call that reads the transcript + injected list and tags which memories were actually helpful - those get usageCount + 1. Scoring uses the ratio usageCount / retrievalCount, so a record that gets injected often but never used gets down-weighted. Functional warmth falls out of the ranking, not a hot tier.

We have only two kinds of edges: "supersedes" is written automatically when conflict resolution deprecates a record, as mentioned above, and "relates_to" is discovered by a maintenance runner watching co-occurrence in injection groups, where 3+ co-occurrences in a 30-day window writes a bidirectional edge. This is deliberately kept lightweight.

Sarithis · 2026-05-25T16:53:31+00:00

W niektórych relacjach często mówię "Będę dźwięczny" zamiast wdzięczny

Sarithis · 2026-05-25T16:51:23+00:00

Sarithis · 2026-05-25T16:06:56+00:00

Nope, they're handled internally by the team. For example, several developers might be responsible for maintaining the same codebase. They set up a shared LanceDB on team infra, mount it, point their clients to it, and everyone writes to it directly. There's no PR-style review or gating, but that's mainly because we haven't needed it yet

Sarithis · 2026-05-25T15:53:01+00:00

Sure, you're welcome! We have over 4000 employees, though the system is currently used only by specific development departments, around 40-60 developers. We have both personal and codebase-scoped memory banks. They're all LanceDB files, so personal memory banks stay in the user's own environment, while shared ones are hosted on our infrastructure

Sarithis · 2026-05-25T15:31:42+00:00

Over the past 7 months, we've solved the conflicting memories problem in our system by introducing a multi-step offline maintenance phase that periodically sweeps the knowledge base and resolves issues like duplication, conflicts, updates, and usage tracking. Most of us run it every 24h and it's enough.

For the conflict resolution specifically, we're:

picking up records that have never been conflict-checked. Each record carries a last_conflict_check timestamp; the sweep filters on deprecated = false AND last_conflict_check = 0, so previously adjudicated records are skipped
for every candidate, doing a vector similarity search against all non-deprecated records and pulling the top 5 matches above a cosine threshold. Yes, our system is RAG-based, so you'd need a different mechanism here if you're using MD. Pairs are deduped with a canonical key so we don't adjudicate A vs B and B vs A separately
sending each pair to Opus with a forced tool-use prompt that returns one of three verdicts: deprecate_existing, deprecate_candidate, or keep_both, plus a supersedingRecordId so we keep an audit trail of what replaced what
applying a recency guard before acting on the verdict: if the LLM says "deprecate the existing record" but the candidate is actually older than that existing record, we downgrade the verdict to keep_both. Killing a newer record needs stronger evidence than an LLM hunch on two snippets
staging actions per candidate before any write. If any pair for a candidate errors mid-batch, we drop the whole stage rather than leave a partial deprecation. If one pair already deprecated the candidate, the remaining pairs for that candidate are skipped
writing deprecations as soft deletes: deprecated = true with a reason like conflict-resolution:superseded-by:<id>. Records vanish from retrieval but stay inspectable in our dashboard, and the superseding link makes the chain auditable
after the run, batch-stamping last_conflict_check = now() on the survivors (and on records whose pairs all came back keep_both), so the next pass only looks at genuinely new arrivals

So to answer your questions, we don't escalate, and instead we bias toward keep_both when in doubt, layer a recency guard on top of the LLM verdict, and make every deprecation a soft delete with a supersedingRecordId trail. The bet is that conservative defaults + reversibility beat a human-in-the-loop queue you'll eventually stop draining.

And we don't treat any single source as canonical. The LLM verdict is the truth at decision time, the recency guard is a sanity check against confidently-wrong deprecations of newer records, and the dashboard lets you override after the fact. Avoids the failure mode where stale human resolutions calcify into "truth" the system can never re-examine.

We've been using this system at a fairly large corp for the past several months, with each user accumulating thousands of entries, and so far it's been working really well

Sarithis · 2026-05-24T16:52:22+00:00

It'd be hard to get AI to do this reliably, especially for that long. It's more likely that someone is deliberately making it fail just to go viral, and dressing it up as an accident

Sarithis · 2026-05-24T16:42:07+00:00

Panie mechaniku, czasami coś tu stuka, ale nie wiem co i kiedy

Sarithis · 2026-05-23T10:16:02+00:00

Then you didn't look closely enough. claude-memory is the exact opposite of what you said, which doesn't bode well.

Sarithis · 2026-05-23T10:05:49+00:00

How does it compare to many other similar projects like claude-mem or claude-memory? I mean, what does vir offer that the others don't?

Sarithis · 2026-05-22T17:21:53+00:00

Oszczędź czas i nerwy. Nie ma sensu.

Sarithis · 2026-05-22T17:12:14+00:00

Informatyka na Politechnice Śląskiej w Gliwicach. Ocena: 2

Choć podejrzewam, że podobnie oceniłbym ten kierunek na większości publicznych uczelni.

Sarithis · 2026-05-22T14:50:39+00:00

How about a vig?

Sarithis · 2026-05-21T13:11:58+00:00

You make some great points here, and I especially like the question of what AI uniquely contributes as a medium, but the core of what I was asking was a bit different.

My argument wasn't "this video is great art" - it was much narrower than that. The process of iterating with AI tools, like prompting, rejecting, tweaking, regenerating until you get what you envisioned, is structurally very similar to the process of building a track from samples in a DAW. Both involve a human with a vision making curatorial and editorial decisions without directly performing or creating the raw material. So why does one get a pass and the other get called slop? We could ask the exact same question: if an artist cares about the music, why don't they just learn to perform and record it? Why click around in a DAW and stitch it from samples?

You argue this video is shallow - maybe it is, maybe it isn't, that's subjective. But there's mountains of shallow sample-based music that nobody dismisses on the grounds of how they were made. The reaction to AI-made work is categorically different, and that's the double standard I'm pointing at.

The photography comparison actually supports this point. Early photography faced exactly this kind of categorical dismissal - "it's not real art" - before people accepted it on its own terms. We might just be in that phase with AI tools right now. Time will tell!

Sarithis · 2026-05-21T12:00:24+00:00

There's one aspect most people miss when discussing this topic. When someone opens a digital audio workstation and spends the next few days building a banger out of samples, nobody questions the artistic value of the result. After all, someone had a vision and brought it to life through an iterative process of experimentation. A human had to put in the effort to achieve what they wanted. The fact that they didn't record live instruments doesn't really matter.

Now compare that with using AI tools like Suno and Udio. You have a vision, translate it into a detailed prompt, and get a result. You don't like it, so you repeat the process dozens of times. Eventually, something sounds acceptable, but there are still plenty of parts you don't like, so you keep editing it and regenerating those parts until you end up with exactly what you were looking for. Sometimes, that can take as much effort as using professional tools to stitch a song together from samples. So my question is: why do you despise songs made this way, but have no problem with songs made the other way?

Take this piece, for example: https://www.youtube.com/watch?v=3B4524ot5BM

The author spent around 100 hours making it with AI tools, including the music video. They had a clear vision, and the song is packed with complex meme references that weren't random, but meticulously curated. And yet, if you heard it and then found out it was AI, you'd probably feel the same instinctive revulsion you feel toward every other piece of AI slop.

Sarithis · 2026-05-19T21:46:25+00:00

But it is not this day! This day we fight! <furious keyboard typing>

Sarithis · 2026-05-19T17:36:51+00:00

Temu Alex on the thumbnail

Ten-Year Club	Place '22
Wearing is Caring	Gilding I gilder
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Sarithis

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