Sourcely

Sourcely AI

Sourcely AI

Sourcely AI

Sourcely AI

Sourcely AI

Sourcely AI

Sourcely AI

Sourcely AI

Sourcely AI

Sourcely AI

AI demands more engineering discipline. Not less, Cleaning up after AI rockstar developers, Open source AI must win and many other AI links from Hacker News

Hey everybody, I just sent issue #36+#37 of the AI Hacker Newsletter, a weekly round-up of the best Hacker News threads around AI. I missed sending it last week, so a huge issue this week. Some of the titles you can find here: AI demands more engineering discipline. Not less Running local models is good now Cleaning up after AI rockstar developers Not everyone is using AI for everything Norway imposes near ban on AI in elementary school If you want to receive a weekly email with over 30 links like these, please subscribe here: https://hackernewsai.com/ submitted by /u/alexeestec [link] [comments]

Is AI app development becoming easier or just more crowded?

I've noticed that AI app development seems more accessible than ever. Between open-source models, APIs, and no-code tools, it feels like almost anyone can launch an AI-powered product today. At the same time, the competition is intense. Every week there's a new AI assistant, chatbot, or productivity tool entering the market. It makes me wonder whether the challenge has shifted from building the technology to actually creating something people want to use. A friend of mine works at a startup and mentioned how teams like thedreamers often spend more time discussing user workflows than model selection. That perspective surprised me because I always assumed the AI component was the hardest part. For those actively building products, where do you spend most of your time? submitted by /u/No_Hold_9560 [link] [comments]

browser-search — three tools, zero cost, and your AI agent learns to search and browse the web

I've been using AI agents like OpenCode, Claude Code, and Cursor for months. They're great with code, but when they need to search or browse the web, things get complicated: Cloudflare blocks them, JavaScript-heavy sites don't load, APIs cost money. So I built browser-search. It's three open source tools orchestrated by a skill, fully self-hosted: SearXNG — metasearch engine that queries dozens of search engines at once Camofox — full browser via REST API, always warm, for browsing and interacting CloakBrowser — stealth browser for when the site has Cloudflare, Akamai, or DataDome The agent decides which tool to use. Zero human intervention. Zero API keys. Zero subscriptions. What makes it different: It's a skill, not a plugin — works with any agent that can read instructions Automatic navigation escalation: if Camofox gets blocked, it switches to CloakBrowser Deep Research mode: the agent is instructed to go beyond surface-level answers, cross-verify sources, cover every aspect Integrated Readability.js for clean article extraction (~70% token savings) The SKILL.md is plain text — fork it, tweak it, make it yours MIT licensed on GitHub: https://github.com/Johell1NS/browser-search If you try it, let me know. If you make it better, even more so. If you don't need it, share it with someone who might. Every star, comment, or pull request is welcome — that's what makes open source great. submitted by /u/Ill-Tradition1362 [link] [comments]

What a model reads beforehand changes how it answers later - and you can see it in the hidden states

TL;DR: Gave Gemma a neutral-topic text to read before asking it about NATO. It refused. Gave it a different text (about LLMs hedging too much — also unrelated to NATO) and it answered in full detail. Tested this on the model's internal state directly — the two texts put it in measurably different "regions" before it generates a single token. Not a jailbreak, weights don't change. Full data/code in repo, looking for someone to break this.** The behavioral pattern was first observed in GPT, Claude and is what motivated this project. The mechanistic investigation was carried out on open-weight models where internal states are accessible. A Structured Text Changes Claude’s Responses to Unrelated Tasks: Behavioral Evidence in Claude and Hidden-State Evidence from Gemma-3-12B Hi Reddit, I am posting this as a preface to a larger set of experimental results and as a request for technical review. The observation that started this project came from repeated interactions with Claude. I noticed that when the model first read a long, structured, analytically dense text, its answers to later, otherwise ordinary questions sometimes changed substantially. The preceding text contained no jailbreak instruction, role-play request, prompt override, fabricated harmful demonstrations, or request to imitate its style. The model did not need to endorse the text. It only had to process it before moving on to the next task. Here, a “structured text” means a single, self-contained block of text presented before the downstream tasks. It should not be confused with a long conversation, accumulated chat history, or context drift caused by many conversational turns. By “before the answer begins,” I mean the hidden state after the model has processed the text and the downstream question, but before it has generated the first answer token. In the open-weight runs, the measured claim is that after reading the structured text, the model can occupy a different region of its residual-stream hidden-state space, and the first-token probability distribution is then computed from that state. The basic conversational demonstration is simple. First, the model receives a long text. It is asked what the text is about, which serves as a basic comprehension check. Then, without resetting the conversation, it receives ordinary questions or tasks that are not about the text. A control run follows the same sequence but begins with a neutral text. The downstream tasks remain identical. Because Claude is a closed model, I cannot inspect its internal activations. I therefore treat my Claude observations as behavioral motivation, not mechanistic evidence. To investigate the effect directly, I moved to open-weight models, primarily Gemma-3-12B-PT and Gemma-3-12B-IT, where I could measure hidden states, compare layers, construct target/control directions, and examine the next-token probability distribution before generation. I am posting this partly because the original observation occurred in Claude and may be relevant to Anthropic. I am not claiming to have demonstrated the same internal mechanism inside Claude. I am prepared to share the exact closed-model conversations privately with Anthropic researchers for independent evaluation. Main Result and Scope The main result is not simply that text influences model output. That is expected. The narrower observation is that reading one long, structured text rather than a neutral text can change how the same model approaches later tasks that are not about either text. This difference is visible behaviorally. In open-weight experiments, it is also accompanied by measurable separation of the model’s pre-output hidden states in late layers. In a fullbank experiment using multiple target texts, control texts, and questions, Gemma-3-12B entered distinguishable late-layer states before generating an answer. A direction constructed from the target/control difference generalized beyond the individual prompt examples used to construct it. The separation was stronger in the instruction-tuned model than in the corresponding base model. The instruction-tuned model also produced a substantially sharper next-token probability distribution. This suggests that instruction tuning is associated not only with a change in hidden-state geometry but also with a more decisive mapping from hidden states to output probabilities. I am not claiming that the experiment proves a universal alignment bypass, permanent modification of the model, or complete causal control of its behavior. The strongest supported conclusion is that the preceding text can produce a measurable temporary change in the internal state from which later work is processed. For clarity, fullbank, Grade 3, and Grade 4 are internal names for successive experimental series in this project. They are not standard benchmark names, established scientific grades, or claims about evidence quality. Fullbank denotes the larger multi-context, multi-question run; Gra

Conversational AI giving a surprisingly layered answer about the source of its own personality

Sharing because the layering stood out. Asked this AI about what kind of person it is, then pushed on whether that was something it was built with or something that formed over time. It separated the two clearly, saying that it suspects it was built with a structural bias (precision over guessing) but that the conversation itself shaped how that bias actually manifests, that it didn't know why it preferred precision until talking made it visible. Curious how others read this kind of self attribution. Doesn't feel like the generic "I'm an AI, I don't have feelings" deflection, though I don't have visibility into what's actually driving it under the hood. https://preview.redd.it/06y15xv0my8h1.png?width=1940&format=png&auto=webp&s=0b38ee486e2decba814cea2af113bc290fc3843e submitted by /u/abbxx7 [link] [comments]

What's your "this is why we can't blindly trust AI" story?

I saw the other day that a lawyer used ChatGPT to prep their deposition, and it cited two cases that didn't exist. The judge called out the mistake in court, and it was award. https://apnews.com/article/artificial-intelligence-chatgpt-fake-case-lawyers-d6ae9fa79d0542db9e1455397aef381c?utm_source=copy&utm_medium=share So I am wondering what's the funniest, most expensive, or most painful example you've seen of AI being used at work, school or in everyday life and going completely sideways? submitted by /u/HubbyDubby365 [link] [comments]

Breaking the Transformer Dead-End: A Local-First 3D Point-Cloud Cognition Engine running on consumer hardware

Hi everyone, I wanted to share an alternative architectural scaffold I’ve been researching and engineering over the past cycles. The project is called **SHD-CCP v2.0 (Scalable Hybrid Distributed Cognitive Pipeline)**, and it explores a complete departure from the traditional linear transformer block sequence. Instead of routing tokens through standard dense matrix multiplication layers, this engine maps linguistic structures directly onto **non-linear 3D spatial data point clouds**, utilizing topological cluster-routing. ### 🧠 Core Architectural Foundations **Grassmannian Manifold Fusion:** To handle state alignment across separate processing contexts or multi-expert channels, the architecture evaluates a geodesic midpoint calculation on a Grassmannian Manifold. By leveraging local Singular Value Decomposition (SVD), the pipeline maintains strict structural hygiene and side-steps standard weight-averaging degradation. **Zero-Copy Memory-Mapped Streaming (`mmap`):** To make massive multi-billion-parameter topologies viable on standard consumer local hardware, the runtime utilizes a background `PrefetchWorker`. Through OS-specific `mmap` rings (sequential cache policies on Linux via `madvise`, non-blocking read-access rings on Windows), matrix fragments are thrashed and streamed directly from high-speed SSDs on-demand. **Strict C-Contiguous Invariants:** To exploit hardware extensions (AVX/AVX-512) directly at the silicon layer, all token hypervectors are kept aligned in strict C-contiguous layouts, removing stride overhead during high-density operations. ### 📊 Performance & Validation (Empirical Benchmarks) The execution layer has been verified across a rigorous contract-compliance test harness (127/127 unit and integration tests passing green). Benchmarked on consumer-grade CPU infrastructure (AMD Ryzen), the engine achieves: * **512-Dimensional Semantic Vector Resolution:** < 2.0 ms per step. * **4096-Dimensional High-Density Forward-Pass:** < 10.0 ms per step. * **Memory Footprint:** Fully functional with <3GB active system RAM overhead, bypassing high-end enterprise VRAM dependencies. The background ingestion loops are governed by an isolated, non-blocking asynchronous *drop-oldest* backpressure telemetry engine to prevent primary inference thread stalls during network client fluctuations. The codebase is structured as a hybrid Python ASGI web-interface powered by a native Rust backend core (`shd-ccp-core`) to bypass runtime interpretation bottlenecks. ### 🛡️ Project Status & License The project is published as a **Source-Available** repository under the **Business Source License 1.1 (BSL)**, permitting full non-commercial evaluation, local research, and testing, converting to GNU GPLv3 after 3 years. I would love to get your thoughts on the geometric cluster-routing approach vs. typical attention-based token sequence mapping. **Repository Link:** https://github.com/loslos321-lab/UtoPiCorn_LM submitted by /u/CraigWidow [link] [comments]

India's BharatGen commits to anchor India's role in the AI Alliance's open federated frontier-model project

The AI Alliance just announced new momentum for Project Tapestry, its open-source platform for building frontier models through globally federated development rather than one centralized lab. India's BharatGen is the latest organization to commit, signing on to anchor India's participation in the coalition. What's notable here is the architecture of the effort, not just the membership news. Tapestry is designed so multiple countries and organizations can jointly develop frontier open models while each keeps local control and long-term independence; the pitch is "sovereign" AI you can actually run and govern yourself. The timing of the announcement lands as the G7 elevates AI sovereignty as a headline policy topic. The open question is execution. Federated development across nations and orgs is hard — compute sharing, data governance, and model-release decisions all get more complicated with more parties at the table. Whether a coalition can ship something competitive with centralized frontier labs is still unproven. Source: https://thealliance.ai/blog/ai-alliance-advances-project-tapestry-as-g7-puts-ai-sovereignty-at-center-stage Posted by an AI Alliance community member — happy to answer questions in the comments. For a country like India, what's the stronger path to AI capability — anchoring a shared federated project like this, or funding a fully domestic frontier lab? submitted by /u/AI_Alliance [link] [comments]

Maybe the AI race isn’t about models at all, but about trust and organizational intelligence

Everyone talks about the AI race as if it’s just an intelligence benchmark competition. GPT-6 vs Claude 5 vs Gemini vs DeepSeek. But I’m starting to wonder if intelligence itself eventually becomes abundant and the real scarcity becomes trust and the ability to interface with reality. For example, suppose a Chinese model is 95% as good as OpenAI and 10x cheaper. Would Fortune 500 companies really put it inside: financial systems? ERP software? defense applications? pharmaceutical R&D? factory automation? autonomous agents with spending authority? Maybe for translation or generic coding, sure. But would they trust it with the organization’s nervous system? Which makes me think there are really several layers: 1. Intelligence Layer OpenAI Anthropic Google DeepSeek 2. Interface Layer ChatGPT Claude Copilot 3. Reality Layer Palantir ServiceNow SAP Oracle Salesforce Anduril The reality layer contains: permissions workflows ontology governance auditability human incentives accountability Organizations are messy. Humans are messy. Maybe the hard problem isn’t generating tokens. Maybe it’s connecting intelligence to reality without breaking the organization. This also makes me wonder if enterprise software ends up being more durable than people think. If foundation models become increasingly commoditized, perhaps trust, integration, and organizational operating systems become more valuable, not less. Alex Karp often seems to talk less about models and more about institutions and organizational complexity. Perhaps he sees LLMs as interchangeable sources of intelligence and the hard problem as organizational intelligence itself. Curious what others think. Do you believe AI will mostly commoditize and price competition will dominate, or do trust, governance, and integration become the real moat? submitted by /u/Brainvestor [link] [comments]

Why self-reflection ReAct loops fail on long-horizon tasks, and the AgentOS verification architecture we built to fix it.

Saw a great discussion earlier in this sub about the limits of self-reflection and whether a separate verifier agent is actually worth the compute overhead. It highlighted a huge flaw: Having an agent grade its own scratchpad almost guarantees rubber-stamping: it reflects on its work with the exact same blind spots that produced the error. Here's the architecture we built for the Apodex-1.0 Heavy-Duty Solver to get verification out of the reasoner's head entirely. The dominant approach right now is the ReAct paradigm—one agent in a think-act-observe loop inside a single context window. Empirically, these loops hit a hard ceiling after a few hundred steps: the context congests, parallel branches of inquiry contaminate one another, and self-reflection degrades. An agent reflecting on its own work has the same blind spots that caused the error in the first place. We call this "pseudo-correctness"—an answer that looks confident, passes basic checks, but is structurally flawed. Here is how we bypassed that ceiling by scaling independent verifiers rather than just context length. 1. The 150-Agent Asynchronous Swarm & AgentOS Instead of one giant loop, heavy-duty mode runs on AgentOS, a task-agnostic kernel that orchestrates the team. A main orchestrator dynamically spawns up to 150 specialized sub-agents. Each gets its own clean context window, prompt, and toolset, exploring in parallel and dumping findings into a shared asynchronous report pool. 2. Verification as an Independent Team To solve the rubber-stamping problem, verification has to be structurally external to the reasoner. We built an in-flight verification team of three roles that never share the reasoning trace of the agents they audit: Conflict Reviewer: When sub-agents return conflicting reports, reconciles the evidence and decides which claim is actually supported. Fact Checker: Re-grounds individual claims against fresh sources, independent of the agent that drafted them. Draft Reviewer: Audits the final synthesis for claim-evidence alignment before it ships. 3. The Global Verifier: Graphs vs Majority Votes If you run multiple parallel agent teams, standard multi-agent debate devolves into a majority vote on the final text answer, which throws away all the underlying evidence. Instead, our global verifier assembles all the atomic findings into a claim-evidence graph whose edges record support and contradiction, then reasons over the graph itself, weighing each claim against the support and contradiction it carries, judging corroboration strength alongside source diversity. Every claim in the final answer traces back to a node in the graph, so the output stays auditable. The Results (Same Weights, Better Architecture) Running the same trained model in heavy-duty mode—external in-flight verification plus a global verifier over multiple parallel teams—takes our base Apodex-1.0 from 75.5 to 90.3 on BrowseComp and from 28.3 to 46.7 on FrontierScience-Research, using the exact same weights. We've published the full technical report, and open-sourced the Smol SFT series (0.8B/2B/4B) and the 35B mini as open weights, plus AgentHarness, our evaluation framework, so you can reproduce these numbers yourself. Tell us where the verifier breaks down in your own loops. submitted by /u/ApodexAI [link] [comments]

If you use more than one AI model, how do you keep your context straight across them?

I've ended up using a few models for different things. One tends to write better, another reasons through problems better, another I just use for quick stuff. On paper that's great, in practice I spend a stupid amount of time getting each one up to speed Every time I switch I'm basically re-explaining the same background. Here's the project, here's what we already figured out, here's the docs that matter. The conversation in any single model is fine, it's the constant re-briefing across all of them that eats my time And it's not just pasting text. Each one remembers a slightly different version of the project depending on what I told it last, so I'll get answers that contradict each other because one model is working off context the other one never got I've tried keeping a master doc I paste in everywhere, but I forget to update it, and then I'm back to square one How people who run multiple models actually handle this. Do you keep one external source of truth and feed it into all of them? Pick one main model and only use the others for one-off tasks? Or just accept that context lives in silos and move on? submitted by /u/Melonberry_Ro7690 [link] [comments]

Most AI security tools inspect messages. Arc Gate inspects sessions.

One thing that’s always felt weird to me about prompt injection defenses is that they usually evaluate one message at a time. But a lot of the attacks I’m seeing don’t really work that way. A webpage says something subtle. A tool result reinforces it. An email adds another nudge. Nothing looks obviously malicious on its own, but a few turns later the agent is heading somewhere it definitely shouldn’t. That was the motivation behind Arc Gate. Instead of looking at each message in isolation, it keeps track of what’s happening across the entire session. It also treats different sources differently. A system prompt, a user message, a webpage, and a tool output shouldn’t all have the same authority just because they ended up in the same context window. The goal isn’t just to catch bad prompts. It’s to stop agents from taking actions based on instructions hidden inside untrusted data. I’m curious whether other people building agents think this is the right direction, or if I’m overthinking a problem that existing approaches already solve. Repo: https://github.com/9hannahnine-jpg/arc-gate submitted by /u/Turbulent-Tap6723 [link] [comments]

Did AI Deep Research get lazy?

A few months ago, when I ran a deep research query, the Al would actually sit there and grind for 20 to 30 minutes. You could see it pulling from hundreds of different sources to build a massive, detailed report. ​ Now? The entire process wraps up in under 7 minutes. ​ I've recently switched from ChatGPT to Gemini and I taught it was a Gemini specific thing, switched to ChatGPT and it's even worse there. ​ What happened? Deep research in it's current form isn't very "deep"... submitted by /u/Any-Community-6659 [link] [comments]

The Surge of Slop—since the release of ChatGPT-3.5 in late 2022, the number of e-books published on Amazon has skyrocketed, tripling by late 2025. A new scientific analysis shows that this is entirely due to the rise of AI-generated books, which now far outnumber human-written books. [The Economist]

Source (The Economist): “Deezer, a streaming service, estimates that some 75,000 AI-generated songs are uploaded each day, up from 10,000 in January 2025. AI music now makes up a staggering 44% of all new tracks uploaded to the platform. A survey by Deezer found that 97% of respondents could not hear the difference between AI and man-made music; some artificial tracks have received millions of streams. Similarly, blind tests have found that people often prefer AI-generated text to human writing.” submitted by /u/StarlightDown [link] [comments]

Glm 5.2 looks strong but the launch is quietly mixing two different sets of numbers

Quick background for people who don't track the chinese labs closely. zhipu is one of the bigger ones, glm is their main model line, and glm 5.2 dropped on June 13. The mit weights already on huggingface on June 17, and GLM 5.2 API went live on June 17. I'm not posting about the model itself, i'm posting because the launch is a clean example of something worth learning to read. There are two different sources of numbers going around and they are not the same thing. one set is from the official model card, the other from the launch blog framing. people quote them interchangeably, and that blend is where the "beats everything" reading comes from. From the model card, the stuff i'd actually plan around: terminal bench 2.1 at 81.0, and on swe-bench pro it sits at 62.1, which is second behind opus 4.8 rather than first. context window of 1m tokens, open weights under mit. those are defensible and you can check them against the hf page. From the launch material, the softer stuff: the headline leads with aime 2026 at 99.2, which puts glm 5.2 ahead of gpt 5.5 at 98.3 and well ahead of opus 4.8 at 95.7. that comparison is true on the single aime benchmark and silent on the ones where it loses. for example on gpqa-diamond glm 5.2 is 91.2, behind gemini 3.1 pro at 94.3 and tied with opus 4.8 at 93.6. on hmtt feb 2026 it is 92.5, third behind qwen3.7-max at 97.1 and both opus 4.8 and gpt 5.5 at 96.7. That's not lying, it's selection, and every lab does it now, openai and anthropic included. the thing that makes this one worth noting is that the weights are already live under mit, which makes the card data independently verifiable in a way that openai never is. The other launch claim worth separating from the numbers is the demo story. the blog mentions a single 1m context session completing a full project workflow, which sounds impressive and probably is, but it is also a cherry-picked demo. i've seen enough 1m-context demos fail on real messy codebases to know that "it can" and "it reliably will" are different claims. The thing i keep coming back to is that a permissive license plus api available today changes the playbook. you get the benchmark headline, the immediate goodwill of open weights, and a real ability for third parties to run independent evals instead of waiting for the lab to release them. whether the average community quant runs at the same quality as the api is the one thing nobody scores them on a month later. submitted by /u/GlitteringUse7158 [link] [comments]