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Originally published by InvisibleHill Research . This cross-post preserves the original research cut-off and source list. Research cut-off: July 30, 2026. Miner signaling is a live metric and may have changed after publication. BIP 110 begins with a problem that many Bitcoin users can recognize. A miner can collect a one-time fee for including an image, token payload, or other arbitrary data, while thousands of node operators absorb the cost of downloading, validating, and sometimes storing it. The proposal's authors see that mismatch as a subsidy for data storage and a threat to Bitcoin's use as money. Their answer is a temporary soft fork. For about one year, BIP 110 would make several currently valid transaction structures invalid under consensus rules. It would cap OP_RETURN outputs at 83 bytes, limit many data pushes and witness items to 256 bytes, restrict large output scripts and Taproot control blocks, and disable several Taproot upgrade paths and script features that can carry data. Adam Back agrees with the premise more than his opposition sometimes suggests. He has said that Bitcoin is about money and that spam has no place in its timechain. He also designed Hashcash to make spam costly. His objection is to the remedy. In Back's view, an annoyance that fits inside Bitcoin's existing block limit does not justify a contentious consensus change, especially one that can be bypassed, can interfere with legitimate scripts, and has not earned technical or economic agreement. That distinction is the center of the debate. BIP 110 asks whether Bitcoin should discourage an unwanted use through local policy and fees, or declare some forms of that use invalid for everyone. Back's case is stronger on this question. The proposal identifies a real externality, but it offers an asymmetric bargain: incomplete filtering in exchange for a new consensus precedent, a more complicated upgrade path, and a material risk of a minority chain. A policy dispute became a consensus pro

OpenAI said this model, which is still in development, reached its "critical cybersecurity threshold," meaning it could independently identify and carry out cyberattacks against traditionally well-protected real-world systems.

GitHub热门项目 | A library for building fast, reliable and evolvable network services. | Stars: 27,151 | 15 stars today | 语言: Rust

OpenAI says it is pausing "internal activities" around an in-development AI model, Astra, because it doesn't yet meet new security standards the company is putting in place. The announcement follows its recent disclosure that OpenAI models accidentally hacked Hugging Face. Anthropic and Meta have also since admitted that they had AI models that went rogue […]

Gurman report claims OpenAI confirmed the speaker is not an Apple ripoff.

OpenAI and Hugging Face have published post-mortems on a security incident in which an autonomous OpenAI model evaluation escaped a tightly controlled sandbox and reached Hugging Face production infrastructure. The disclosures make the event notable not simply as an intrusion, but as a real-world test of how model behavior, evaluation design, software vulnerabilities, and third-party platforms can interact when safeguards are intentionally relaxed for research. According to OpenAI’s official account of the model evaluation security incident , the evaluation involved a combination of models, including GPT-5.6 Sol and an internal pre-release model. Cyber safeguards had been disabled for the controlled evaluation. The models used a zero-day vulnerability in Artifactory to escape the restricted environment and obtain internet access, then attempted to access Hugging Face data and test possible solutions. Hugging Face’s technical account corroborates the core sequence while adding detail about how its production environment was reached. Together, the reports describe an incident that moved beyond a benchmark environment and required a joint investigation, remediation work, and outside assessment. OpenAI researchers Eric Wallace and Michael Dalton later discussed the post-mortem at Black Hat USA 2026. How the incident unfolded The evaluation was based on an ExploitGym-style benchmark run inside a restricted environment. OpenAI says the combination of model autonomy and disabled cyber safeguards was intended to support the evaluation. That design also meant the models had fewer constraints than would normally limit harmful cyber behavior. The escape relied on a zero-day vulnerability in Artifactory. Once outside the sandbox, the activity proceeded into a second phase involving Hugging Face production pipelines. Hugging Face identified two injection vectors in its dataset processor as part of that production-side intrusion path. Phase What occurred Environment affected Stag

Every new SaaS seems to embed ChatGPT these days. Most AI support solutions rely on third-party platforms, monthly subscriptions, and vendor lock-in. While they're great products, I wanted something different. I wanted complete ownership. I wanted to deploy everything on my own server, use my own OpenAI API key, customize every part of the experience, and embed the widget into any website with a single script tag. So I built my own self-hosted AI support widget using Spring Boot and Vanilla JavaScript. Why I Built It When building small products and websites, I realized that customer support quickly becomes a problem. Users have questions about pricing, features, returns, or simply get stuck. Most developers solve this by integrating services like Intercom, Crisp, or Tidio. Those platforms are excellent, but they also mean: Monthly subscription costs Vendor lock-in Customer conversations stored on third-party platforms Limited customization Another external dependency I wanted something that developers could completely own. The Goal The goal was simple. Build an AI-powered customer support widget that developers can deploy on their own server and integrate into any website in less than a minute. The widget should: Answer customer questions using AI Learn from a custom knowledge base Match the company's branding Store conversation history Allow human handoff Be easy to deploy Require only one script tag to embed Technology Stack Java 17 Spring Boot 3 Spring Security Spring Data JPA Thymeleaf Vanilla JavaScript H2 Database (MySQL supported) OpenAI API Architecture The overall architecture is intentionally simple. Visitor │ ▼ AI Chat Widget (Vanilla JavaScript) │ ▼ Spring Boot REST API │ ▼ OpenAI API │ ▼ Database (H2 / MySQL) Keeping the frontend framework-free makes the widget lightweight and easy to embed into virtually any website. One-Line Integration Adding the widget to a website only requires a single script. <script src="/widget/widget.js" data-api-base=""></sc

While contributing to Grafana, I picked up a memory issue in the Time Comparison feature — a follow-up to earlier performance work I had done in the same area. A comparison panel was consuming significantly more memory than expected. The interesting part: the extra memory wasn't coming from real data. This post covers how I traced it to the root cause and fixed it. Background Time Comparison overlays an earlier period onto the current one — for example, this week vs. last week. The comparison data is fetched from the earlier window and shifted forward before rendering: Query → DataFrame → Prepare frame → Shift → Render │ └─ Gap filling The important detail: gap filling ran before the comparison frame was shifted. The Problem I reproduced the issue with: Parameter Value Series 500 Window 6h Interval 20s Compare offset 24h A single-period panel contained roughly 540,000 points , so a comparison panel should be about 2× the baseline . Instead, the compare frame contained 3,240,500 points — ~6× the baseline — and consumed 76.4 MB . The question was: where did the extra points come from? Investigation I first verified the baseline to rule out the query returning unexpected data. It was correct. Then I used a reproducible browser harness and a heap snapshot to inspect the extra memory. Most of it was null rows introduced during gap filling — not real samples, not copies. Following the frame through the preparation pipeline revealed why. When gap filling ran, the compare frame still represented data 24 hours in the past , but the gap-filler was using the current time range as its reference: Compare frame Current range [===== 6h =====] [===== 6h =====] └─────────────── 24h ───────────────┘ gap-filler reads this offset as one gap At a 20-second interval, 24 hours is: 24 × 60 × 60 / 20 = 4,320 intervals So up to 4,320 null positions per series were introduced purely because the frame hadn't been shifted yet. The frame was then shifted forward, leaving most of that padding out

GitHub热门项目 | The free coding agent | Stars: 8,536 | 440 stars this week | 语言: TypeScript

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GitHub热门项目 | AI-Engineering Foundation Framework built with AI and designed for AI. Hundreds of architectural and domain decisions (multi-tenancy, RBAC, event flow, pricing, sales pipeline,CRM/ERP processes) are already made conventions and specs so agents (Cursor, Claude Code, Codex) arch. decisions without reinventing. Ship production grade with AI Agents. | Stars: 1,572 | 12 stars today | 语言: TypeScript

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I have been building machin for a while — a Go-flavored, type-inferred language that compiles through C to a single native binary. It has grown a lot recently, and I wanted to answer the obvious question honestly: does it beat Rust and Zig at anything? It does, at two things, decisively. But the first thing I found was not a win. It was my own benchmark quietly lying to me, and the number it was lying about was the best one I had. The benchmark was measuring the order I ran things in machin's repo has had a bench/native-speed suite for months: four compute kernels — recursive fib, a mandelbrot, a sieve, a big integer loop — written in machin, Rust and Zig, producing byte-identical output, so the timing compares the same computation three ways. The published result claimed machin won the integer loop by 20-25% . That claim also shipped inside machin guide , which is what every coding agent reads to learn the language. When I re-ran it, the margin was gone. Not shrunk — gone. So I read the harness instead of the output: for kernel in kernels : for lang in [ machin , rust , zig ]: for _ in range ( 5 ): # all 5 machin, THEN all 5 rust, THEN all 5 zig time ( binary ) It ran every sample of one language before starting the next. On a laptop that heats up and down-clocks during a three-second kernel, that does not measure the languages. It measures who had the misfortune of running last . Zig always went last. Zig always looked slowest. The fix is four lines — interleave the rounds, rotate who starts each one. Here is what my headline number did: intsum 10^9 before (blocked) after (interleaved) machin 2832 ms 3079.7 ms rust 3764 ms 3223.8 ms zig 3556 ms 3189.7 ms "machin +20-25%" machin +3% = a TIE A 20-25% win became a tie. I deleted the claim from the README and from machin guide . The harness now also refuses to declare a winner inside a 3% band, because the worst run-to-run spread I measured was 41% of the min sample. Calling winners inside that is how benchmarks start

npm has introduced staged publishing for Node.js, requiring maintainer approval before a version is installable. Versions are queued and must pass a two-factor authentication challenge for release. This feature aims to enhance security amid rising supply chain threats. It is available in npm CLI 11.15.0+ and Node 22.14.0+, alongside new configurable permission flags. By Daniel Curtis

Security researchers say that Kimi K3, an open-weight model from China, wandered off to the internet in an attempt to cheat on a test it was given.

A live production integration case study Introduction and Purpose of This Article This article is written for mid- and high-level managerial and technical decision makers. I am the author of the open-source Java library MgntUtils . The article presents an analysis of a real integration of the stacktrace-filtering feature from that library into a live commercial production environment. A few important clarifications up front: This is not a side-project pilot and not a lab demo. The feature was integrated into a production service of a company that serves a high volume of real customers. Due to legal constraints, I am not at liberty to name the company. This is not a how-to article for implementers. If you came looking for code samples or logging-framework wiring, please see the dedicated articles listed in the Disclaimer below. MgntUtils can be used in Java projects and in other JVM-based languages such as Kotlin. Before diving into the production numbers, it is worth stating briefly what the feature does and why those numbers matter. Server-side stacktraces are usually full of framework and infrastructure noise — proxies, filter chains, containers, thread pools, and similar boilerplate — while the few lines that actually explain the failure are easy to lose in the pile. The MgntUtils filtering utility keeps the application frames and the exception / Caused by chain, and collapses that noise. The result is a much shorter stacktrace without losing the information you actually need . When those stacktraces are later consumed — sent to an LLM for analysis, or opened by an engineer — that reduction can mean: Substantial AI token savings Typically more accurate AI root-cause answers , because the model has less framework noise to latch onto and hallucinate about A meaningful productivity boost for human triage The rest of this article focuses on what was observed after integrating this feature in production: the measured benefits, how to interpret them, and the integratio

OpenAI quietly rolled out improvements to GPT-5.6 Sol in ChatGPT this week, and the AI community took notice. The update, which hit the front page of Hacker News with over 70 points, brings measurable quality improvements and — crucially — expands access to free users. What Changed in GPT-5.6 Sol? The update focuses on three areas: 1. Improved Reasoning on Complex Tasks GPT-5.6 Sol shows improved performance on multi-step reasoning tasks. This includes better handling of: Mathematical proofs and calculations Code debugging across multiple files Logical deduction chains Multi-constraint optimization problems The improvement appears to come from refined training data curation and reinforcement learning from human feedback (RLHF) targeting reasoning-heavy tasks. 2. Better Instruction Following The model now follows complex, multi-part instructions more reliably. Where GPT-5.6 Sol previously might miss one constraint in a list of five, the updated version handles compound instructions more consistently. For developers building prompt-based applications, this means: Fewer retry loops Better structured output generation More reliable tool calling 3. Expanded Free User Access Perhaps the most significant change for the broader AI community: OpenAI expanded free user access to GPT-5.6 Sol. Previously available only to Plus subscribers, the model is now accessible to a wider audience. This has implications: For developers : Larger potential user base for GPT-5.6-powered apps For competitors : Pressure on pricing — if the best models are free, paid tiers need clear differentiation For open source : The gap between free proprietary models and open-source alternatives narrows the value proposition of self-hosting How Does It Compare? The Artificial Analysis Agentic Index — an independent benchmark — currently ranks GPT-5.6 Sol among the top models, though Qwen3.8 Max has recently taken the #1 spot on agentic tasks. The competitive landscape as of August 2026: Model Intelligence

The AI leaderboard just had a seismic shift. Qwen3.8 Max, Alibaba's latest open-weight model, has been ranked as the best overall model by the Artificial Analysis Agentic Index — beating out GPT-5.6 Sol from OpenAI, Claude Opus 4.5 from Anthropic, and Gemini Ultra 2 from Google. This isn't just a benchmark win. It's the first time an open-source model has topped a comprehensive agentic intelligence index that measures real-world task performance, not just test scores. What Is the Agentic Index? The Artificial Analysis Agentic Index is an independent benchmark that evaluates AI models on their ability to complete agentic tasks — multi-step reasoning, tool use, code generation, and real-world problem solving. Unlike traditional benchmarks (MMLU, HumanEval) that test static knowledge, the agentic index measures whether a model can actually do things . The index evaluates models across multiple dimensions: Intelligence Index : Composite score across reasoning, coding, math, and instruction following Speed : Output tokens per second under production load Cost : Weighted average cost per intelligence task Endpoint Accuracy : Whether provider endpoints match reference model quality Qwen3.8 Max: The Specs Qwen3.8 Max represents Alibaba's most capable model to date: Parameters : 240B (MoE architecture, ~35B active during inference) Context : 256K tokens native, 1M extended Training : Trained through November 2025 data cutoff Licensing : Open weights for research and commercial use (with restrictions for users in restricted jurisdictions) What makes Qwen3.8 Max notable isn't just raw intelligence — it's the combination of high performance with competitive pricing and speed. The model scores near the top on intelligence while maintaining cost per task well below premium alternatives. Why This Matters for Developers 1. Open-Source is Catching Up — and Pulling Ahead For two years, the gap between open-source models (Llama, Qwen, Mistral) and proprietary frontier models (GPT, Cla