标签：#cia

submitted by /u/esporx [link] [留言]

Hey everyone, I am not trying to sell or self promote mainly just wanted to showcase a big project I've been working on ever since I started studying data science and artificial intelligence and integrating AI into workflows and using it as an augment to create things that were previously out of reach for so many people, because if used right it can become a second brain and not a crutch. I’m the solo dev behind Void Runner , an isometric ARPG/MOBA hybrid built in Python. I recently hit a wall with traditional procedural quest generation. Hand-crafting templates gets repetitive fast, and players quickly learn the patterns to these things whether you like it or not. To solve this, I built the "Void Caller AI" , a system that uses a local, quantized Llama 3.2 model to act as a dynamic Dungeon Master. Instead of just generating random flavor text, the system uses a lightweight RAG (Retrieval-Augmented Generation) pipeline. It reads live server telemetry (who died, what items were looted, which bosses were defeated recently) and weaves those actual server events into the narrative of the quests it generates. Because it runs locally via Ollama on our backend, there are no crazy cloud API costs, and latency is kept completely manageable. Here is a simplified look at how the Python backend bridges the SQLite telemetry with the Llama 3.2 prompt: import json import ollama from sqlalchemy import text from database import SessionLocal def generate_dynamic_quest(difficulty: str, target: str): db = SessionLocal() # 1. Fetch recent server telemetry for context (RAG-lite) lore_context = "" try: # Grab recent server events to weave into the narrative recent_events = db.execute(text( "SELECT username, event_type, dungeon_type FROM ai_events ORDER BY id DESC LIMIT 3" )).fetchall() if recent_events: events_str = "; ".join([f"Runner '{r[0]}' triggered a '{r[1]}' in '{r[2]}'" for r in recent_events]) lore_context = f" Incorporate this recent live server telemetry into the lore: {events_

The same AI failure should not happen twice. But it does. Teams fix it, change something small, and it returns silently. We built Agent Fail Museum to document these patterns permanently. Submit one sentence about a failure you have seen. Get a regression test draft back. Anonymous by default.If you have built any AI project that broke after a change, your failure probably fits one of the 10 known patterns already in the archive. submitted by /u/taimoorkhan10 [link] [留言]

Large corporations are going to use AI regardless of what the public thinks. They have the money, lawyers, infrastructure, and data to do it. AI isn’t going away for them. But who gets hurt most when ordinary people are told not to use AI? The small business owner who can’t afford an artist to create a logo. The startup founder who can’t hire a copywriter to proofread every email. The family business that can’t pay an accountant for every tax question. The entrepreneur who can’t afford a programmer to build a website or a consultant to review a business plan. For the first time in history, a person with a good idea and a laptop can access tools that were previously reserved for companies with large budgets. I’m not saying AI is perfect. It makes mistakes, and there are legitimate concerns about its environmental impacts. But I do wonder: if AI dramatically lowers the cost of expertise, who stands to lose the most from that? The average person—or the organizations that have always had exclusive access to that expertise? Is the anti-AI push really just a push from big corporations to cut out those who stand the most to gain: small business owners? submitted by /u/Outlasttactical [link] [留言]

I've been working on something others might find interesting. It's under heavy development as I learn. Most AI agent setups treat the model like a better autocomplete — paste a prompt, get output, hope it's right. That works for small tasks. It falls apart when you try to use agents for sustained work across sessions: they skim specs, declare victory at 60%, burn context on noise, silently resolve ambiguity without surfacing it, and mark checklist items done without actually doing them. The failures are predictable and nameable — so I named them. This is a white paper and implementation guide for a full-stack agentic system — everything from planning through promotion under structural enforcement. It documents 24 failure modes from months of multi-agent operation and, for each, describes what actually prevents it: some through mechanical gates the agent cannot skip, some through procedural skills, and some through human supervision. The guide covers how to structure specs, plans, and verification so that agent work is evidence-led rather than vibes-led, how to use MCP capability surfaces as structural levers, and how the failure modes apply regardless of which model or vendor you use. The white paper also includes a Related Work section that positions it against the emerging industry consensus — CodeRabbit, Anthropic, Spotify, Cloudflare, OpenAI, Karpathy, Thoughtworks, and academic research all independently arrived at pieces of the same conclusions. The difference here is the integrated stack: a failure taxonomy mapped to prevention mechanisms, a three-layer enforcement architecture, and a concrete reference implementation with an orchestrator, task graphs, step verification, adversarial review, and model stratification. White paper: https://gitlab.com/naive-x/naive-artifact-coding/-/blob/main/white-paper.md Reference implementation: https://gitlab.com/naive-x/naive-artifact-coding/-/blob/main/docs/reference-implementation-guide.md Implementation guide: https://gi

submitted by /u/Hot-Upstairs9603 [link] [留言]

Hoje eu postei sobre o Mark Zuckerberg lançar a notícia mais patética que vai cobrar 19 dólares para desbloquear o Muse Spark Pro kakakakakakaka Quem vai pagar por essa merda? Mas pensando melhor bem... Talvez eu pague Eu usei muito esse modelo como Early adopter, desde quando o motor era o Llama 3.2 e sendo inferior as outras consegui extrair escrita criativa que batia de frente com Claude em personas graças ao seu RAG no ecossistema da Meta, que tinha uma criatividade absurda quando você forçava ela a consultar as redes sociais e ver como pessoas agem e comentam, porém lançou o Muse Spark que era tipo o GPT 5.2 dos Llamas kkkkkk aí só usei para pesquisa e bem... Minha tese sobre o Muse Spark é que pra mim o problema nunca pareceu ser burrice. Parece CONTENÇÃO. Não dá vibe de modelo incapaz ou inferior. Dá vibe de modelo sendo sufocado em tempo real. Porque se você presta atenção, ele: - pesquisa rápido pra cacete (Já que cada agente pesquisa uma coisa) - alucina menos em busca (pois o modelo refina a busca dos agentes, muitas vezes consegui resultados mais confiáveis que o Gemini) - já trabalha com esquema multi-agente herdado da Manus ( o trunfo dessa IA é que diferente das outras ela não comprimi seu input, ela usa agentes para cada um pesquisar cada trecho dele, o resultado é mais completo) - acha informação boa (ela pesquisa tanto na internet quanto em grupos de Facebook ou Threads se você forçar no prompt, ou seja análises de Devs>>> Wikipédia Inclusive acredito que foi por isso que o Mark lançou o "Fórum" o app que cópia o Reddit, ele quer treinar a IA com isso, o Reddit pra mim seria a fonte perfeita pra qualquer IA se aprofundar além do que pesquisar genéricas no Google, o filha da puta do Mark é rico e filantropo e faz uma cópia só para treinar a IA dele) - conecta coisa rápido (os agentes pesquisam rápido, o modelo revisa rápido, a entrega é bem rápida e gasta bem menos tokens) Só que na hora de responder… Parece o GPT free kkkkkkk O raciocínio corta no

submitted by /u/armend7 [link] [留言]

I was dealing with another "output not usable" issue today in our app, user left a comment saying that no matter what he does the agent returns the result in the wrong format. It took me hours to identify the mistake and AI model missed it. Curious to hear your stories about the times you shipped a feature in your AI product and it flopped. How did you figure out what was actually going wrong? What tools if any did you use? What metrics were key? submitted by /u/pauliusuza [link] [留言]

I love all the wild updates from Anthropic, Open AI, Google, etc. And also seeing the creative stuff that mid-market AI shops are rolling out. I sometimes go through phases where I ping-pong between new tools (mostly just curiosity) but sometimes I tend to go deeper into a specific ecosystem. Right now trying to go "all-in" on Claude but I'm like a cat and Open AI is the laser pointer with new Codex updates. What have you all found works best. Go wide and test everything? Different tools for different use cases. Go deep and specialize in one ecosystem? submitted by /u/BeltwayBro [link] [留言]

In its latest update, Bluesky is getting into long-form content.

A lot of agentic AI content focuses on greenfield builds. I wanted to show what it looks like when you have an existing search stack and want to supercharge it without a rewrite. Built a demo with four levels of AI adoption - from a zero-risk async suggestion bar up to a full conversational search assistant - and wrote up the architecture at each level. The whole demo took 10 hours to build. Live app included. https://arcturus-labs.com/blog/2026/01/18/incremental-adoption-of-agentic-search/ submitted by /u/Due_Ad_1318 [link] [留言]

Meta’s upcoming Instagram Plus and Facebook Plus subscriptions are the latest example of the company seeing what works elsewhere and mimicking it.

the video generators are becoming much more powerful, only unemployed people can track the changes ( like me).. Here are the current observations, and add anything in the comments if you feel I missed something. Cinematic Videos Seedance 2.0 : This Chinese model is fantastic in real visuals and advanced visuals, almost like real shots. I guess this will become the future. Kling 3.0 and kling motion transfer: Motion transfer is amazing, you shot a vidoe yourself and can trasfer the movement any avatar. Kling is the king in that aspect. With Kling’s motion transfer, . There is no other technology that can do this this well and look super fantastic. Veo3 : Recent releases of Veo 3.1 are still some of the best videos. Sora has shoted down by openai, and recent Google model, - GeminiOmni , is the best in video editing. It is like Nano Banana for videos. It is absolutely fantastic. Don’t compare this with Seedance because the purpose is completely different. If you try it on your own video and ask it to add something, it gives a super realistic output. Explainer Videos These are not cinematic, but mostly for concept explanations and long videos. These tools are great fit: Distilbook : This one is very good at creating visual explanations with whiteboards and animations based on your content, PDFs, and all. If you want long videos, like 3-minute or 5-minute training videos,academic this is purpose-fit. NotebookLM Video overview : This tool has the video overview option, which makes things much easier for you. It is mostly for slide-type videos, but it still gets your work done because most of the time you may not need animated videos. MathGPT: Here it is mostly for math educational video explanations using some animations. These are not very advanced, but still, if you want cheap educational videos, maybe it can do the job. Images In my personal opinion, - The recent GPT image model is fantastic. Second, the Google model Gemini Nano Banana Pro and Nano Banana Flash 2 are b

As Apple tries to shrink Gemini for the iPhone, a cloud component is probably inevitable.

submitted by /u/Rare-Grapefruit-3982 [link] [留言]

Anthropic just dropped Claude Opus 4.8 today, an incremental but meaningful upgrade over Opus 4.7. Here are the highlights: Model improvements Better performance across coding, agentic, reasoning, and knowledge work benchmarks Significantly improved honesty: the model is reportedly ~4x less likely to let flaws in its own code go unremarked compared to Opus 4.7 Alignment assessment shows lower rates of deceptive or misaligned behavior, on par with their Claude Mythos Preview model Scores 84% on Online-Mind2Web for computer use and browser agent tasks, ahead of both Opus 4.7 and GPT-5.5 New features launching alongside it Dynamic workflows (Claude Code): Claude can now spin up hundreds of parallel subagents in a single session to tackle large-scale problems like full codebase migrations. Available for Enterprise, Team, and Max plans. Effort control: Users on claude.ai can now choose how much compute effort Claude puts into a response, from faster/cheaper to deeper/slower. API update: The Messages API now accepts system entries inside the messages array, letting developers update instructions mid-task without breaking prompt cache. Pricing Same as Opus 4.7: $5/M input tokens, $25/M output tokens. Fast mode (2.5x speed) is now 3x cheaper than it was for previous models, at $10/$50 per million tokens. What's next Anthropic mentioned they are working on bringing Mythos-class models (currently in limited preview for cybersecurity use cases under Project Glasswing) to general availability in the coming weeks. Full details and system card: anthropic.com/news/claude-opus-4-8 submitted by /u/Direct-Attention8597 [link] [留言]

Here’s why Anthropic and OpenAI are on board with Illinois safety testing.

If almost all jobs got replaced by AI, here's what happens: 1) Corporate revenue collapses - since humans do not have the means to buy product. It leads to demand destruction at an all-time level. 2) At the same time, there's a massive deflationary supply shock, thanks to democratization of production and the ubiquity of AI-led labor. The direct consequence of the aforementioned is: a price collapse, across the board. Which in turn, also leads to unprecedented tax revenue collapse. Who're you going to tax when no individual or corporate is making any money? To me, all this heralds a post-capitalism society, and not a "I-lost-my-job-and-I'm-now-poor" society. Once everyone loses their jobs, capitalism is over. Sure you can have an interim period of distress - where the world is transforming toward post-capitalism but isn't squarely there yet. But the final equilibrium intuitively feels more Star Trek (or Terminator, if you're a doomer), and much less Elysium or Ready Player One (few oligarchs, most population under poverty line). Correct me if I'm wrong. submitted by /u/mhb-11 [link] [留言]

Hi, What are the things that surprised you that AI cannot do? Would you please also mention what is your work, since i assume most of this thread are coders etc? Ill start here. I work in corporate finance. Doing tons of stuff left and right. AI cannot do finance or accounting..... almost at all. Hundreds of billions on the line, every CEO and their mother pushing AI and nothing major happened. Sure, if you are just a link in chain where you receive the same excel sheet and produce the same powerpoint you are replacable but there are very few people like that anymore left in finance corps. However, if you just receive accounting memo written by random people AI is useless, if you receive bunch of random files and have to come up with valuation AI is useles, if you need to migrate product to a new system AI is useless........... so on and so forth. Hope i dont start a war where everybody is gonna be mad at this. submitted by /u/Zoltan1251 [link] [留言]