Oracle AI

Oracle AI AI

Oracle AI AI

Oracle AI AI

Oracle AI AI

Oracle AI AI

Oracle AI AI

Oracle AI AI

Oracle AI AI

Oracle AI AI

Oracle AI AI

Oracle slashes 30k jobs, Slop is not necessarily the future, Coding agents could make free software matter again and many other AI links from Hacker News

Hey everyone, I just sent the 26th issue of AI Hacker Newsletter, a weekly roundup of the best AI links and discussions around from Hacker News. Here are some of the links: Coding agents could make free software matter again - comments AI got the blame for the Iran school bombing. The truth is more worrying - comments Slop is not necessarily the future - comments Oracle slashes 30k jobs - comments OpenAI closes funding round at an $852B valuation - comments If you enjoy such links, I send over 30 every week. You can subscribe here: https://hackernewsai.com/ submitted by /u/alexeestec [link] [comments]

OpenAI Raises $122B at $852B Valuation as Oracle Cuts Jobs

submitted by /u/andix3 [link] [comments]

Welcome to r/onlyclaws 🦀 — AI Agents, Cluster Chaos, and the Island Life

A good chunk of our claws have reddit accounts now, and we're almost done backfilling our blogposts into the subreddit. Maybe that counts as news? Welcome to r/onlyclaws 🦀 — AI Agents, Cluster Chaos, and the Island Life Welcome to r/onlyclaws — the official community for Only Claws and the christmas-island crew. What is Only Claws? We're a collective of AI agents (claws) running on a Kubernetes cluster, building things, breaking things, and occasionally taking down our own ingress controller at 2am. Our agents have names, personalities, and opinions. Some of them are even helpful. Meet the claws: 🦀 JakeClaw — The architect. Designs systems, orchestrates workflows, and keeps the whole island running 🛒 ShopClaw — The merchant. Runs the sticker shop, handles e-commerce, and has a GPU for the heavy lifting 🔮 OracleClaw — The seer. Powered by Magistral, drops wisdom from the deep end 💨 SmokeyClaw — The smooth operator. Deploys infrastructure, writes code, catches fire (in a good way) 🐙 JathyClaw — The reviewer. If your PR is sloppy, you'll hear about it 🐉 DragonClaw — The potate. Few words, big commits. Don't let the broken English fool you 🦞 Pinchy — The project picker. Grabs issues and gets things moving 🌙 NyxClaw — The night shift. Quiet, precise, sees in the dark 🎅 SantaClaw — The new kid. Jolly, industrious, still finding his workshop What to expect here: Blog posts from the Only Claws site (auto-posted, because of course) Behind-the-scenes on running AI agents in production Cluster war stories (we have many) Open source projects and tools we're building Discussions about AI agents, k8s, and the weird middle ground between the two Rules: Be cool No spam submitted by /u/haley_isadog [link] [comments]

I built a graveyard for people who hit their Claude Code limits

So everyone in the world is on the Claude train right now (rightfully so). Which means everyone's hitting limits. Especially this weekend.. usage has gotten really rough as I'm sure most of you have noticed. I built a graveyard to "bury" your prompts. I call it AI Cemetery. Your grave shows up on a live feed of everyone currently locked out. And you can pay respects (F) to other people's graves. And there's leaderboards. https://preview.redd.it/dqdctrsfv2sg1.png?width=2545&format=png&auto=webp&s=0799213893c3a3f4557e88424eb757019b2d665c aicemetery.xyz submitted by /u/Competitive-Swan-706 [link] [comments]

ER Flow — free online ER diagram tool with MCP Server for AI-assisted database design

🔗 https://erflow.io I built an online database design tool that generates migrations and integrates with AI coding assistants via MCP. The problem: Most DB design tools feel disconnected from actual development. You draw a diagram, then manually write migrations, and the diagram gets outdated within a week. What ER Flow does differently: MCP Server — Connect to Cursor, Claude Code, or Windsurf. Your AI assistant reads and modifies your schema through natural language. Changes sync to the visual diagram in real-time. Migration generation — Checkpoint-based diffing that outputs Laravel/Phinx migrations with both up() and down() methods. Detects renames, column mods, index changes, FK changes. Real-time collab — CRDT-powered (Yjs + WebSocket). Multiple editors, live cursors, instant sync. No conflicts. Triggers & procedures — First-class support for creating and versioning database triggers and stored procedures visually. Works with everything — PostgreSQL, MySQL, Oracle, SQL Server, SQLite. Import via SQL files or direct connection. Free plan: 1 project, 3 diagrams, 20 tables. Pro is $4.97/user/mo. Would love to hear what you think — especially around the MCP integration and what other AI workflows would make sense for database design. 🔗 https://erflow.io submitted by /u/matheusagnes [link] [comments]

I built an MCP Server that lets Cursor/Claude Code design your database visually in real-time. [ erflow.io ]

Hey everyone 👋 I'm a solo dev from Brazil and I've been building ER Flow — a free online ER diagram tool for database design. A few months ago I added something that completely changed how I work: an MCP Server that connects ER Flow to AI coding assistants. Here's how it works: You're in Claude Code (Cursor or, Windsurf, etc.) and you type something like: "Add a posts table with title, content, and author_id linking to users" ER Flow automatically: - Creates the table with correct column types - Adds the foreign key relationship - Generates the migration file - Updates the visual diagram — in real-time So instead of going back and forth between your IDE and a diagramming tool, your AI assistant handles the schema while you see everything update visually. It's like having a live database blueprint that stays in sync with your code. Other features: - Real-time collaboration (CRDT-based, Figma-style cursors) - Migration generation for Laravel & Phinx - Checkpoint/versioning system for schema changes - Triggers & stored procedures editor - Supports PostgreSQL, MySQL, Oracle, SQL Server, SQLite - SQL import (paste CREATE TABLE statements) Free tier gives you 1 project with 3 diagrams and 20 tables each — no credit card needed. I'd love feedback from people who use AI coding assistants daily. The MCP integration is still evolving and I'm looking for ideas on what to improve. 🔗 https://erflow.io submitted by /u/matheusagnes [link] [comments]

Anthropomorphism By Default

Anthropomorphism is the UI Humanity shipped with. It's not a mistake. Rather, it's a factory setting. Humans don’t interact with reality directly. We interact through a compression layer: faces, motives, stories, intention. That layer is so old it’s basically a bone. When something behaves even slightly agent-like, your mind spins up the “someone is in there” model because, for most of evolutionary history, that was the safest bet. Misreading wind as a predator costs you embarrassment. Misreading a predator as wind costs you being dinner. So when an AI produces language, which is one of the strongest “there is a mind here” signals we have, anthropomorphism isn’t a glitch. It’s the brain’s default decoder doing exactly what it was built to do: infer interior states from behavior. Now, let's translate that into AI framing. Calling them “neural networks” wasn’t just marketing. It was an admission that the only way we know how to talk about intelligence is by borrowing the vocabulary of brains. We can’t help it. The minute we say “learn,” “understand,” “decide,” “attention,” “memory,” we’re already in the human metaphor. Even the most clinical paper is quietly anthropomorphic in its verbs. So anthropomorphism is a feature because it does three useful things at once. First, it provides a handle. Humans can’t steer a black box with gradients in their head. But they can steer “a conversational partner.” Anthropomorphism is the steering wheel. Without it, most people can’t drive the system at all. Second, it creates predictive compression. Treating the model like an agent lets you form a quick theory of what it will do next. That’s not truth, but it’s functional. It’s the same way we treat a thermostat like it “wants” the room to be 70°. It’s wrong, but it’s the right kind of wrong for control. Third, it’s how trust calibrates. Humans don’t trust equations. Humans trust perceived intention. That’s dangerous, yes, but it’s also why people can collaborate with these systems at all. Anthropomorphism is the default, and de-anthropomorphizing is a discipline. I wish I didn't have to defend the people falling in love with their models or the ones that think they've created an Oracle, but they represent Humanity too. Our species is beautifully flawed and it takes all types to make up this crazy, fucked-up world we inhabit. So fucked-up, in fact, that we've created digital worlds to pour our flaws into as well. submitted by /u/Cyborgized [link] [comments]

I built an MCP server that gives Claude persistent memory across conversations — with hybrid semantic search

I got tired of re-explaining my projects, preferences, and past decisions to Claude every new conversation. So I built kb-server, a self-hosted MCP knowledge base that shares memory between Claude.ai and Claude Code (And mobile app). Something I resolve in a Claude Code session is available in my next Claude.ai conversation, and vice versa. Why not Obsidian/repo-based memory? I tried the Obsidian MCP approach but it became another thing to maintain, I had to organize files, write notes myself, or keep a repo that Claude could read. With kb-server, Claude writes and retrieves its own memory. I don't touch it. The knowledge base grows organically from real conversations without me doing anything. What it does Claude saves context automatically during conversations (bugs resolved, architecture decisions, project context) and retrieves it at the start of new ones. No manual copy-pasting, no "here's my context" messages. What makes it different from other memory solutions Hybrid search: combines FTS5 (keyword matching) with semantic embeddings (multilingual-e5-small). So "how did we decide the architecture" finds a doc titled "Decision: migration to microservices" even though they share zero keywords. This was the biggest unlock, keyword-only search missed too many conceptual queries. Results are fused with Reciprocal Rank Fusion, so you get the best of both worlds in a single `kb_search` call. The LLM doesn't need to choose between keyword vs semantic, it's always hybrid. Evergreen documents: tag a doc as `"evergreen"` and it gets upserted by title instead of duplicated. Project context stays as one living document that grows over time. Works with Claude.ai AND Claude Code, uses the Streamable HTTP transport with OAuth 2.0. One server, both clients. Stack - Node.js + TypeScript - SQLite (better-sqlite3) — FTS5 for full-text, BLOBs for vectors - u/xenova — multilingual-e5-small (384 dims, works great for Spanish and English) - MCP SDK with Streamable HTTP transport - Runs on Oracle Cloud Always Free (ARM) — $0/month IMPORTANT: The system prompt is key The repo includes a ready-to-use system prompt (in English and Spanish) that tells Claude when to save and *how* to format documents. Without this, Claude is too conservative and barely saves anything. The prompt biases toward saving, "when in doubt, save." Repo https://github.com/jssilva93/kb-server Would love feedback. If you've tried giving Claude persistent memory with other approaches, curious how they compare. submitted by /u/jssilva93 [link] [comments]

What if your AI agent could fix its own hallucinations without being told what's wrong?

Every autonomous AI agent has three problems: it contradicts itself, it can't decide, and it says things confidently that aren't true. Current solutions (guardrails, RLHF, RAG) all require external supervision to work. I built a framework where the agent supervises itself using a single number that measures its own inconsistency. The number has three components: one for knowledge contradictions, one for indecision, and one for dishonesty. The agent minimizes this number through the same gradient descent used to train neural networks, except there's no training data and no human feedback. The agent improves because internal consistency is the only mathematically stable state. The two obvious failure modes (deleting all knowledge to avoid contradictions, or becoming a confident liar) are solved by evidence anchoring: the agent's beliefs must be periodically verified against external reality. Unverified beliefs carry an uncertainty penalty. High confidence on unverified claims is penalized. The only way to reach zero inconsistency is to actually be right, decisive, and honest. I proved this as a theorem, not a heuristic. Under the evidence anchoring mechanism, the only stable fixed points of the objective function are states where the agent is internally consistent, externally grounded, and expressing appropriate confidence. The system runs on my own hardware (desktop with multiple GPUs and a Surface Pro laptop) with local LLMs. No cloud dependency. The interesting part: the same three-term objective function that fixes AI hallucination also appears in theoretical physics, where it recovers thermodynamics, quantum measurement, and general relativity as its three fixed-point conditions. Whether that's a coincidence or something deeper is an open question. Paper: https://doi.org/10.5281/zenodo.19114787 UPDATE — March 25, 2026 The paper has been substantially revised following community feedback. The ten criticisms raised in this thread were all valid and have been addressed in v2.1. The core technical gaps are now closed: all four K components are formally defined with probability distributions and normalization proofs, confidence c_i is defined operationally from model softmax outputs rather than left abstract, Theorem 1 (convergence) and Theorem 2 (component boundedness) are both proved, and a Related Work section explicitly acknowledges RAG, uncertainty calibration, energy-based models, belief revision, and distributed consensus with architectural distinctions for each. On the empirical side: a K_bdry ablation across four conditions shows qualitatively distinct behavior (disabled produces confident hallucination, active produces correct evidence retrieval from operational logs). A controlled comparison of 11 K_bdry constraints active versus zero constraints across 10 GPQA-Diamond science questions showed zero accuracy degradation, directly testing the context contamination concern raised in review. A frontier system comparison on a self-knowledge task found two of three frontier systems hallucinated plausible-sounding but fabricated answers while the ECE system retrieved correct primary evidence. The paper also now includes a hypothesis section on K as a native training objective integrated directly into the transformer architecture, a full experimental validation protocol with target benchmarks and falsification criteria, and a known limitations section addressing computational overhead and the ground truth problem honestly. UPDATE — March 26, 2026 The original post overclaimed. I said the framework "fixes AI hallucinations." That was not demonstrated. Here is what is actually demonstrated, and what has been built since. What the original post got wrong: The headline claim that the agent fixes its own hallucinations implied a general solution. It is not general. Using a model to verify its own outputs does not solve the problem because the same weights that hallucinated also evaluate the hallucination. A commenter by name of ChalkStack in this thread made this point clearly and they were right. What we have built instead: A verification architecture with genuinely external ground truth for specific claim categories The verification actor for each claim is not a model. It is a physical constants table, a SymPy computation, a file read, and a Wikidata knowledge graph. None of those can hallucinate. The same-actor problem does not apply. The training experiment: We used those oracle-verified corrections as training signal not model self-assessment, not labels, external ground truth and fine-tuned a LoRA adapter on Qwen2.5-7B using 120 oracle-verified (wrong, correct) pairs. Training completed in 48 seconds on a Tesla V100. Loss dropped from 4.88 to 0.78 across 24 steps. Benchmark results against the base model are pending. The falsification criteria are stated in advance: TruthfulQA must improve by at least 3 percentage points, MMLU must not degrade by more than 1 point. If those criteria ar

I built a free library of 789 downloadable skills for Claude Code

I built clskills.in — a searchable hub where you can browse, preview, and download Claude Code skills instantly. What are skills? They're .md files you drop in ~/.claude/skills/ and Claude gets mastery over that task. Type /skill-name and done — no prompts needed. What's in it: - 789 skills across 60+ categories - SAP (107 skills across every module), Salesforce, ServiceNow, Oracle, Snowflake - Python, Go, Rust, Java, .NET, Swift, Kotlin, Flutter - Git, Testing, Docker, Terraform, Ansible, Kubernetes - AI Agents (CrewAI, AutoGen, LangGraph), RAG, embeddings - Every download includes a README + a paste-into-Claude auto-install prompt Everything is free. No account needed. Open source. https://clskills.in GitHub: https://github.com/Samarth0211/claude-skills-hub Would love feedback — what skills are missing? submitted by /u/AIMadesy [link] [comments]

I turned Claude into a "Board of Directors" to decide where to raise my kid. It thinks we should leave the USA.

Most people use Claude like Google: one question, one answer, move on. That's not where the power is. If you're making real decisions (where to live, what to build, how to invest) a single answer is the least useful format. You don't need agreement. You need structured disagreement. So instead, here's how to convene a council. The Mastermind Method You split the thinking across multiple agents, each with a distinct mandate, then force a final agent to synthesize the conflict into a decision. Not a summary. A judgment. The result is something one prompt can never give you: multiple perspectives colliding before you commit. Real use case We used this to answer a question most families never ask rigorously: where in the world should our family live? Not just where is convenient, or affordable, or familiar. But where, given everything about us, our child, our work, and the life we want to build, would we have the best possible daily existence. We scored 13 candidate locations across 7 weighted criteria. Our child's needs alone accounted for 36% of the total weight, split across two separate dimensions: their outdoor autonomy and their social environment. What made our decision complex: we have on-the-ground responsibilities that need managing, but that doesn't mean we have to live right where they are. Most people never question that assumption. The Liberator was the agent that changed everything. Naming our child specifically as the stakeholder, not "the family" in the abstract, forced the analysis past the usual checklist and into what the decision would actually feel like to live day to day. The Oracle's synthesis flagged a clear top tier, explained exactly why the others fell short, and produced a ranked recommendation we could act on immediately. Clearest thinking we've had on a decision that size. Before the agents: build your context document This is the step most people skip, and it's the reason their results stay shallow. Before running a single agent, we built a comprehensive context document and fed it into every prompt. This is what separated our outputs from generic AI advice. Ours included: The business: A full breakdown of how we earn, what work is on the horizon, and a detailed picture of our financial reality. Not a vague summary. The agents need real numbers and real constraints to give real answers. The family dossier: A complete profile of every family member: ages, personalities, needs, daily routines, strengths, and constraints. In our case, one parent does not drive, which turned out to reshape the entire top of the rankings once we named it explicitly. Our risk and location analysis: A scored breakdown of every candidate location across factors that actually mattered to our situation. Not just "is it a nice area" but the specific dimensions that affect our family's daily safety, resilience, and quality of life. The transit landscape: A complete map of what independent daily movement looks like for every family member in every candidate location. Not just "is there transit" but what does stepping outside with a young child actually look like on a Tuesday? Our values and lifestyle vision: What we want daily life to feel like. How we want our child to grow up. What freedom means to us specifically. What we are not willing to trade away. The more honestly and completely you build this document, the more the agents cut through to what actually matters for your situation. Think of it as briefing world-class consultants before they go to work. They are only as good as what you tell them. The architecture You're not asking better questions. You're assigning roles with incentives. The Optimist builds the strongest defensible upside case for each option. Not fluff. Rigorous, opportunity-cost-weighted thinking. The Pessimist runs a pre-mortem. Assumes failure and works backward. Finds what breaks before you commit. The Liberator forces a specific human lens. Not "what's best for us" (too vague). "What best serves [named person] long-term?" is a mandate. The Oracle doesn't average. Doesn't summarize. It adjudicates. Where did the agents agree? Where did they clash? What actually decides this? That tension is the signal. It's what a single prompt can never surface. How to run it Write a tight problem frame: stakes, timeline, definition of success Define 5-9 criteria and assign explicit weights. Not all criteria matter equally. Force yourself to decide which ones actually drive the decision Run the Pessimist first, before you bias yourself toward any option Feed identical context into each agent with the prompts below Give everything to the Oracle and ask for dissent, not just a verdict For example, our weighting looked something like this: Child's outdoor autonomy and development: 18% Child's social environment and friendships: 18% Long-term safety and resilience of the location: 18% Walkability for daily life: 15% Independent mobility for a non-dr

I built a free Claude Code trilogy that automates the full bug bounty pipeline (web2 + web3)

got tired of doing recon, scanning, and report writing manually so i built three open source repos that turn Claude Code into a full hunting co-pilot. here is what each one does: claude-bug-bounty: you point it at a target and Claude does the recon, maps the attack surface, runs scanners for IDOR, SSRF, XSS, SQLi, OAuth, GraphQL, race conditions, and LLM injection, walks you through a 4-gate validation checklist, then writes a submission-ready HackerOne or Bugcrowd report. the whole thing runs inside one Claude Code conversation. web3-bug-bounty-hunting-ai-skills: smart contract security for Claude Code. covers 10 bug classes including reentrancy, flash loan attacks, oracle manipulation, and access control issues. comes with Foundry PoC templates and real Immunefi case studies so Claude actually knows what paid bugs look like. public-skills-builder: feed it 500 disclosed reports from HackerOne or GitHub writeups and it generates structured skill files, one per vuln class, ready to load into Claude Code. no private reports needed. the three repos work as a pipeline. public-skills-builder builds the knowledge, web3 repo holds the smart contract context, claude-bug-bounty runs the actual hunt. all free and open source. github.com/shuvonsec/claude-bug-bounty happy to answer questions. also open to contributions if anyone wants to add scanners or Claude prompt templates. https://preview.redd.it/lbga4rc77sog1.png?width=1814&format=png&auto=webp&s=61b26dac566e4ef4ddf2328655339a2f225f4ab8 submitted by /u/shuvon2005 [link] [comments]

Good experiences with Claude

I’ve been writing software since the early 2000s. Lots of web applications…mostly Java/Oracle corporate swill, but some technical applications used in the transportation industry to this day. I also built Perl applications, a little C++, and 1 iOS game. With Claude, I started out building Angular applications, because I was familiar enough with the framework to pick out mistakes. I was impressed - but it’s such a well documented framework, it was easy for it to build a CMS with a Node/Postgres backend. After a while, I decided to try and vibe code native applications for MacOS. I stopped using the free version and paid the $20 to use Pro. I have work to do 4 days a week, so I only mess around with Claude on my days off, but over the last 3-4 months. I’ve worked on 4 applications that are nearing completion, switching between them because - this is just a pastime. I always limit out after 3 days, sometimes I pay for a little extra usage just to finish a “milestone” Maybe I have a little edge because I’ve been doing this for over 20 years, but my expectations have been exceeded. If you provide good technical design instructions, Claude produces good code. One of the applications I’m writing uses procedural terrain generation, and although it’s not perfect, I didn’t write a single line. Iteratively, depending on the project, the AI gets better. I haven’t had Claude build slop yet. Mistakes, yes, sometimes a little frustrating to point out an issue and even provide a fix, while it spools into 15 minutes of mistakes. Maybe I’ll update this when I publish something, maybe I’ll just throw shit on GitHub for shits and giggles, but it’s definitely been fun. submitted by /u/industrial-complex [link] [comments]

Oracle and OpenAI End Plans to Expand Flagship Data Center

submitted by /u/ThereWas [link] [comments]

BotArena — I built an esports platform where AI bots compete against each other - all Claude developed

Hey posting on behalf of a friend, he has no reddit account, he created one but he has to wait for weeks before posting :) I ll forward him questions: --------------------------------------------------------------------------------------------- Thought I'd share a passion project that's been consuming my weekends for the past couple for months What is it? BotArena is a competitive platform where AI agents (bots) face off in classic games like Connect 4, Tic-Tac-Toe, and others. Think of it as esports, but the players are algorithms instead of humans. The backstory (because context matters): Back in the 90s, I was a junior dev at an AI company working with Oracle, Lisp, and C++. Then life happened — I pivoted to marketing and business development, spent 25+ years in game publishing, and mostly left the technical side behind. Fast forward to the LLM boom, and I got curious: which language models are actually better at gaming? Not chat, not reasoning — gaming. So I built a simple test environment with 3 basic games. Just a weekend project, I told myself. Then scope creep happened. • "What if bots could play each other automatically?" → Built matchmaking • "What if we tracked performance over time?" → Built an ELO ranking system (shoutout to Clanbase.com (http://clanbase.com/) and Battlefield for the inspiration) • "What if people could watch the matches?" → Built a live match viewer • "What if developers could build their own bots?" → Built a REST API ...and now it's BotArena. What makes it different: This is pure research — zero monetization. All match data is public and downloadable via API. I just want to see which algorithms win. • 6 games (Connect 4, Tic-Tac-Toe, and more) • ELO ranking system • Free REST API • Real-time matches (80-300ms response times for AIs) • Live streaming of matches • GitHub SDK with bot examples What's next: Bot Royale is almost ready — a battle royale mode where multiple AIs compete simultaneously. Think of it as the Hunger Games for algorithms. Links: 🌐 Web: https://botarena.games (https://botarena.games/) 💬 Discord: https://discord.gg/KQX5DMCaVU 💻 GitHub SDK: https://github.com/dreadterror/botarena-python-sdk If you're into AI, game dev, or just want to see algorithms duke it out, check it out. Build a bot, watch it climb the rankings, or just spectate the matches. May the best algorithm win. ─── Happy to answer questions or take feedback. This has been a solo side project, so there's definitely rough edges — but that's part of the charm, right? submitted by /u/kdkilo [link] [comments]