标签：#c

EDRSR — the Unified State Register of Court Decisions — is effectively all of Ukraine's judicial practice in open access. Today Qdrant holds **44M+ vectors : criminal (19M), civil (14.3M), commercial (5.1M), misdemeanors (5.6M). Vectorization of civil cases (CPC, justice_kind=1) — the largest cohort at 33.7M documents — runs on a dedicated EC2 instance (r6a.xlarge, 32 GB RAM, 2 TB gp3). Here's what's under the hood: models, pipeline, cost, rakes, and current status. Why Vectorize Courts When a lawyer searches "is there case law on recovering bank prepayment fees" — they don't want to open 40 decisions and read them through. They want the system to surface the top 5 most relevant ones, pull out key paragraphs, and show how courts reasoned. Full-text search (FTS) over keywords doesn't give that — it returns every document containing the word "fee", and there are thousands. For this semantic task you need vector representations of text. The model turns a paragraph from a decision into a point in a 1024-dimensional space; semantically similar paragraphs sit near each other. A kNN search in Qdrant returns the top K nearest, and an LLM composes the answer from exactly those relevant fragments. The only problem: the register is big. Very big. Scale Our prod database holds full texts of decisions starting from 2006. Breakdown by procedural type: Civil (CPC) — 33.7M documents. The largest category. Consumer, housing, labor, family. Criminal (CrPC) — 12M+ Administrative (CAS) — 14M+ Commercial (CC) — 6M+ Misdemeanors (CUaP) — 6M+ The Qdrant collection edrsr_decisions on a dedicated EC2 currently holds 44M+ vectors (122 segments, on_disk=true): | Proceeding type | justice_kind | Vectors | |—|—|—| | Criminal (CrPC) | 2 | 19,036,347 | | Civil (CPC) | 1 | 14,328,427 | | Misdemeanors (CUaP) | 5 | 5,579,432 | | Commercial (CC) | 3 | 5,098,662 | | Total | | 44,042,868 | Civil cases processed: 14.3M out of 33.7M — that's 42%. After CPC completes there will be roughly 63M+ vectors in

In production we have ~1.5 TB of full-text court decisions and their vector embeddings, plus another ~550 GB of other legal data: registries, legislation, business entities, a Spanish case law corpus, EU-Lex. If we take this corpus and train an MoE model the size of DeepSeek V3, scaled to 860B parameters, on GCP — what comes out? We break down the dataset, architecture, compute cost, and the properties such a model would have on Ukrainian law. What's in the Dataset The entire corpus is what's already running in SecondLayer's production. No extra scrapes, no Common Crawl, no noise. EDRSR — the dataset core, ~1.5 TB. The Unified State Register of Court Decisions of Ukraine. 96.2 million full-text decisions (1,079 GB in PostgreSQL TOAST), 471 GB of vectors in Qdrant (voyage-3.5, 1024-dim), 28 GB of metadata (court, judge, date, case category, proceeding type, statute code). Breakdown by jurisdiction: civil 33.7M, administrative 14M+, criminal 12M+, commercial 6M+, misdemeanors 6M+. Largest annual cohort — 2024 (115 GB of TOAST text). OpenReyestr — 43 GB. Ukrainian public registries: 16.7M legal entities (EDR), ownership structures (beneficiaries, shareholders), debtors (State Enforcement Service), NAIS registries. This is the foundation for SneakyPiper — our due-diligence platform — but here it serves as raw corpus for the model. Legislation — ~40 GB. The Constitution, major codes (Civil, Criminal, Criminal Procedure, Civil Procedure, Commercial Procedure, Administrative Procedure, Labor, Tax, Customs), laws, and secondary legislation. All structurally annotated: articles, parts, clauses, revision dates with effective-date tracking. This isn't flat text: we know that Article 124 of the Constitution took effect on a specific date, carries particular references, and is cited in a precise number of decisions. Supreme Court review practices + lu_court_decisions — ~25 GB. SC plenary decisions, practice overviews, Grand Chamber rulings. This is the most valuable slice — the

One quiet handwritten page a day. No feed, no AI. Discussion | Link

I build and run one of the tools on this list (AGenO — full disclosure below), and I use every other tool here regularly. This is what "free" actually gets you on each one, including the catches. The AI tool landscape has a dirty secret: almost nothing labeled "free" is free. Most tools give you a taste — ten messages, three images, one song — and then the paywall lands. So instead of another list of forty tools nobody has tried, here are nine that give you real value at $0, organized by what you're trying to make, with the actual limits spelled out. Quick comparison Tool Best for What's actually free The catch ChatGPT General chat & writing ~10 msgs/5h on the flagship model Silently switches you to a weaker model after the limit Claude Long documents, nuanced writing 10–25 msgs/5h, varies with demand Limits shrink when servers are busy Gemini Image generation & editing Generous with a Google account Best features drift to the paid tier Perplexity Research with citations Unlimited basic searches Pro searches are capped Suno AI music ~10 songs/day No commercial use on free; failed generations can eat credits Leonardo AI Stylized art & game assets Daily token allowance Confusing token system; images are public on free Character.AI Roleplay & AI characters Unlimited chat Heavy filters; your chats train their models AGenO All of it in one place Images, songs with vocals, chat, characters, stories, coding problems — daily free allowance One-person project — busy hours can mean a short queue Canva Magic tools Quick social graphics 50 text-to-image uses Design-tool add-on, not a real generator Chat and writing ChatGPT is still the default for a reason — the free tier includes the flagship model and it's good at nearly everything. The catch nobody tells you about: after roughly ten messages in five hours, it quietly downgrades you to a mini model without making it obvious. If your answers suddenly get dumber mid-conversation, that's why. Claude writes the most natural prose