Your ads, managed by AI that gets smarter daily. Discussion | Link

A process that took 2–3 weeks and produced a 40-page document is the kind of thing everyone wants to "AI-ify." Here's what most people get wrong about it: they reach for the LLM first. In regulated lending, the LLM is the last and smallest part of the pipeline. The hard parts are: ingesting messy documents reliably, extracting data into a canonical model, validating and cross-reconciling it, and doing the actual financial calculations - and those calculations must be done in deterministic code, never by a language model that might quietly round a number wrong. The LLM drafts prose. Code does the math. A human owns the risk decision. That ordering isn't a limitation - under OSFI E-21 it's the only design that's allowed to exist. The teams that win with AI in banking aren't the ones using the biggest model. They're the ones who know which 10% of the problem the model should touch. Where have you seen an LLM used for something that should have been deterministic code? FinancialServices #AI #Lending

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

Local LLM Deployment, Agent Handbook, & LLM Cost Reduction: Applied AI Workflows Today's Highlights This week's highlights cover practical guides for running state-of-the-art LLMs locally and building AI agents, alongside an innovative technique to significantly cut LLM API costs for code processing. These resources focus on actionable insights and frameworks for real-world AI application development. Jamesob's guide to running SOTA LLMs locally (Hacker News) Source: https://github.com/jamesob/local-llm This GitHub repository provides a comprehensive, hands-on guide for setting up and running state-of-the-art Large Language Models (LLMs) on local hardware. It meticulously covers the necessary tooling, dependencies, and configuration steps required to get various open-source LLMs operational without relying on cloud APIs. The guide emphasizes practical considerations for local inference, including hardware requirements, model quantization techniques, and performance optimization for different architectures, directly addressing production deployment patterns. It serves as an invaluable resource for developers and researchers looking to experiment with LLMs, develop applications offline, or reduce costs associated with cloud-based inference by leveraging local compute. The guide offers concrete details and actionable steps, making it an essential resource for anyone aiming to implement LLMs in a controlled, private, or cost-effective environment. Comment: This guide is fantastic for anyone wanting to get serious about local LLM development. It covers the nitty-gritty details of setting up your environment and getting models like Llama-3 running efficiently on consumer hardware, which is crucial for privacy and cost savings. 60% Fable cost cut by converting code to images and having the model OCR it (Hacker News) Source: https://github.com/teamchong/pxpipe The pxpipe project introduces an innovative technique to drastically reduce API costs when processing code with Lar

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

I'm building Guildly, a Slack-like interface where you can run a company of AI employees. I recently used Fable to revamp the whole website into a cool retro pixel style. Comments URL: https://news.ycombinator.com/item?id=48780222 Points: 5 # Comments: 4