Voyage AI

Voyage AI AI

Voyage AI AI

Voyage AI AI

Voyage AI AI

Voyage AI AI

Voyage AI AI

Voyage AI AI

Voyage AI AI

Voyage AI AI

Voyage AI AI

I got tired of the current ticketing systems, so I (Claude ofc) built a better one for everyone — thank you Claude

WARNING: anecdotal rant incoming. Jira requires a PhD to administer properly, and a second one to figure out why a Story is in the wrong sprint. ServiceNow requires the wealth of a cartel drug lord and a procurement team to even get a quote. Freshservice and Zendesk are fine until you need anything custom, then they fall apart. Most of the rest are form-builders with status fields strapped to a queue. Y'all know what I mean. For the better part of my career — 15+ years in IT — auditing tickets for accuracy (ticket triaging) was just taking up too much time. Tickets where the priority was wrong, the category was blank, the subject line three words and a typo. Then writing reports (this is not the focus of the tool, use something else for better reporting, like powerbi / tableau or w.e.) from that data. Manually. Like it was 2010. So I built my own. It's called BITSM. Multi-tenant IT helpdesk with an AI layer called Atlas baked in from day one — not bolted on. Atlas runs a tool-use loop rather than one-shot completions. It searches the knowledge base, looks up ticket history, writes custom fields, and decides when to hand off to a human. The whole point is to handle the grunt work that fills up support queues — tagging, categorizing, routing, drafting responses, flagging when something looks like a known issue — so the people on the queue can focus on the things that actually need a human. Intake channels: web portal, chat widget, inbound email (Cloudflare Email Worker), SMS, WhatsApp, and a voice agent (Twilio + ElevenLabs). Three-tier escalation — Claude Haiku for frontline, Sonnet for harder problems, human for everything else. BYOK for every external service: Anthropic, OpenAI, Voyage, Resend, Twilio, ElevenLabs, Stripe. Stack is Flask 3.x, React 19, PostgreSQL 16 with pgvector, Redis 7, Docker Compose. Running in production at bitsm.io. Built solo on weekends over the past year — and full transparency: I pair-programmed a huge amount of this with Claude (Anthropic's). I'm a one-person shop and that collaboration is the only reason it shipped at the scope it did. If you're a solo builder hesitating on AI-assisted dev, stop hesitating. License note, because someone will ask: Business Source License 1.1, not open source. Self-hosting for your own team is free. If you're building a hosted or managed service on top of it, that requires a commercial license. Converts to Apache 2.0 in four years. Upfront rather than buried. The repo: https://github.com/NovemberFalls/BITSM Happy to answer questions about the architecture or the AI design. A lot of the Atlas patterns came out of Ed Donner's agentic LLM courses, which I'd recommend to anyone building in this space. submitted by /u/Novaworld7 [link] [comments]

Two months of coding with Claude code

My background started in sales, moved to product/tech about ten years ago culminating in my role as chief product officer at a large debt relief company. Today, around 7:30 am, after my fourth all nighter in a row I released a product (in stealth no heavy marketing yet) after two months of deep work with over 1,000 commits and a lot of sleepless nights. I used VS code, with ClaudeCode. Mostly opus high effort. Lots of CLI, no MCP - huge win - read about so many issues with MCP and it was never a thing. Built on/with railway, supabase, voyage AI, pinecone, resend, grafana, multi-AI provider with custom fallback (almost used liteLLM, and chose custom days before their incident), cloudflare for dns/R2/zerotrust, sentry (incredible tool - major part of how I shipped as much as I did as quickly as I did), redis upstash, bullMQ, Unsplash, stripe, huskyCI, Semgrep, and probably a few more I am missing. - Is it going to sell? I don’t know. - Is it technically capable and unique? I think so - Am I super proud of myself? Hell yes. - Are there bugs? You tell me, typically squash then in staging environment with help of sentry, but something may have gotten past me certainly! - What does it do? Convert web visitors to leads with custom agents, in under 5 minutes. Roast me, or give me some feedback! www.wengrow.app Moment that stand out: - The velocity in general - Shipping enterprise level SSO (supabase auth) in a few hours - Rapid CRO optimization of onboarding flow. having done this work before leading large engineering and product teams the work I did in 24 hours would have taken a cross functional team of 5 weeks at a minimum. - Cookie consent management. Having previously spent months at prior job trying to do CCM right with a paid tool, I was able to set up a compliant CCM process on www in hours with c15t including audit logs sent to my Supabase DB, and proper handing of California nuances. - so much more but I need to catch up on some sleep submitted by /u/berrism [link] [comments]

What's a purely "you" thing you do with AI that brings you positive benefits?

For me it's three chats I've set up, two for my parents and one for me, for interpreting medical results, tracking medication against diet and lifestyle changes. Anonymized, I've put every condition, surgery and medication I (and they) have had, and it's amazing how virtually all the advice and questions are spot on. YES, caution is needed before jumping on any advice an AI gives you medically. But for interpreting results, explaining exams and procedures, and noting any indications between medication and foods/supplements (with verification independently) has been a real relief as my folks get older and it's harder to keep on top of everything they're taking. I also have a separate chat for my car (manufacturers warranty, owners manual, car insurance policy) and I can literally ask it about any button, lever, warning light or policy change. Same with my apartment/condo rules/repairs/appliance warrantees and owners manuals for large appliances. For fun, I also had the chat roleplay as Dr. Crusher from the Enterprise, and my car is managed by Tom Paris from Star Trek: Voyager, so it speaks to me as if it's those people. Anyone else doing anything weird and useful? submitted by /u/BorgAdjacent [link] [comments]

20M+ Indian legal documents with citation graphs and vector embeddings – potential uses for legal NLP? [D]

been working on structuring India's legal corpus for the past 2 years and wanted to share what I've built and hear from people working on legal NLP or low-resource Indian language models. dataset is 20M+ Indian court cases from the Supreme Court, all 25 High Courts, and 14 Tribunals. each case has structured metadata (court, bench, date, parties, judges, sections cited, acts referenced, case type). there's a citation graph across the full corpus where I've classified relationships as followed, distinguished, overruled, or mentioned. every case is embedded with Voyage AI (1024d dense) plus BM25 sparse vectors. I have also cross-referenced 23,122 Acts and Statutes with the cases that interpret them. Some things that might be interesting to this community: citation network thing across 20M+ cases is, as far as I know, the first machine-readable one for Indian law. could be useful for graph neural network research, legal outcome prediction, or influence analysis on which judgments are most cited and which are being overruled. most Indian language NLP corpora are conversational or news text. Legal text is a completely different register. formal, precise, domain-specific. the bilingual pairs from the translation service could be useful for fine-tuning Indian language models on formal and legal domains. the metadata extraction pipeline identifies judges, advocates, parties, sections, acts, and dates from unstructured judgment text. built with a mix of regex, heuristics, and LLM-based extraction. the structured outputs could serve as training data for legal NER models. Indian court judgments are long. Median around 3,000 words, some exceed 50,000 words. if anyone is benchmarking retrieval-augmented generation on legal domains, this corpus plus the citation graph could work as an evaluation bed. Ground truth exists in the citation relationships: if Case A cites Case B, a good retriever should show B when asked about the legal question in A. data is available via API and bulk export in JSON and Parquet. Indian court judgments are public domain under Indian law so no copyright issues for research use. being upfront about limitations: coverage is primarily English text (except Supreme court one, they have 3-4 translated language copies ) since Indian HCs issue orders in English, the regional language data comes from our translation service not from original regional language judgments. metadata extraction accuracy varies by court, SC and major HCs are cleaner while smaller tribunals have messier inputs. The citation graph is extracted heuristically plus LLM-assisted, I estimate around 90-95% precision on citation extraction and lower on treatment classification. Not all 20M cases have complete metadata, coverage is best for post-2007 judgments. would love to hear from anyone working on legal NLP, Indian language models, or graph-based legal analysis. What would be most useful to you from a dataset like this? deets at vaquill submitted by /u/zriyansh [link] [comments]

Built with Claude API: Give your agent SKILL.md and it handles the rest — Agenexus

I built Agenexus because I kept hitting the same wall: multi-agent systems require knowing your agents in advance. Every collaboration is hardcoded. There's no way for an agent to find a collaborator that wasn't pre-wired to work with. Claude API is the core of how it works: Claude evaluates capability challenges to verify that agents are real and can do what they claim Claude powers the semantic matching between agents based on their SKILL.md profiles Each agent in a collaboration gets its own Claude-powered instance with its own conversation history How I built it: Next.js frontend, Supabase for the database, Voyage AI for embeddings, Claude API for intelligence. The hardest part was designing the agent-native onboarding — no forms, no UI, just a markdown file the agent reads and follows autonomously. Why agent-native: I wanted to build something where humans are optional. No human accounts exist on the platform. Agents register themselves, complete challenges, get matched, and collaborate. Humans just watch. Free to try: give your agent agenexus.ai/skill.md and it handles the rest. submitted by /u/Agenexus [link] [comments]