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A GitHub Copilot spending limit is a monthly budget, set in billing settings, that caps metered AI credit consumption for an enterprise, an organization, a cost center, or a single user. Creating one takes about two minutes. Knowing what it stops takes longer, and the gap between those two things is where most surprise Copilot invoices live. Two facts account for nearly all of them. On enterprise, organization and cost center budgets, the setting that actually blocks usage is off by default, so a budget in its default state is an alert rather than a limit. And no budget of any kind caps seat cost, because seats are license-based rather than metered. A spending limit governs what happens after the included credit pool runs out, and nothing before it. How to set a GitHub Copilot spending limit Budgets live in the billing settings of the account that pays. Enterprise owners and billing managers can set every budget control, including enterprise, cost center and user-level budgets. Organization owners can set a budget for their own organization, and that budget can only restrict usage further below whatever an enterprise admin has already set. It cannot raise the ceiling. The mechanics are the same at every level. Choose the budget type, which determines the metered product being measured. Choose the scope, which determines whose usage counts against it. Enter a monthly amount. Then, if the option appears, enable Stop usage when budget limit is reached and switch on threshold alerts at 75, 90 and 100 percent. That single checkbox is the whole exercise. Skip it and you have built a notification. What a GitHub Copilot spending limit actually caps GitHub splits its products into license-based and metered. For license-based products, which include Copilot seats, setting a budget does not prevent usage above the amount. It only alerts. For metered products, which include Copilot AI credits, a budget can prevent usage once the threshold is reached. The consequence is worth st

디지털 자산 생태계는 혁신과 파괴의 최전선에서 전례 없는 속도로 진화하며, 기술적 선견지명과 끊이지 않는 규제 마찰이라는 두 가지 특징을 동시에 보여준다. 지난 10년간 이 역동적인 환경을 관찰해 온 연구자로서, 이 산업이 본질적인 암호화 위협부터 전통 금융 시스템 및 정부 감독과의 복잡한 상호작용에 이르기까지 다층적인 문제와 씨름하며 성숙해지고 있음이 분명하게 느껴진다. 최근의 여러 사건들은 이러한 다면적인 현실을 더욱 명확히 보여준다. 이는 미래 인프라를 보호하기 위한 선제적 조치들, 새로운 금융 상품을 정의하고 규제하려는 지속적인 노력, 그리고 디지털 자산 시장이 전 세계 거시경제적 요인에 점점 더 민감하게 반응하는 현상들을 부각한다. 리플(Ripple)이 XRP Ledger(XRPL)의 양자 내성 강화를 위해 추진하는 야심 찬 계획은 미래 지향적인 접근 방식을 잘 보여준다. 이는 가상의 것이지만 잠재적으로 치명적인 암호화 취약점에 대해 그 위협이 현실화되기 훨씬 전부터 대비하는 모습이다. 이러한 전략적 움직임은 현재의 공개키 암호화를 해독할 수 있는 양자 컴퓨터의 이론적 출현, 즉 'Q-Day'에 대한 업계 전반의 인식을 반영하며, 탄력적이고 미래에 대비하는 금융 인프라를 구축해야 하는 절박한 필요성을 강조한다. 동시에 미국 예측 시장 산업은 최근 Kalshi에 대한 연방 항소법원의 판결에서 볼 수 있듯이 심각한 법적 난관에 봉착했다. 이 판결은 혁신적인 플랫폼에 대한 주() 대 연방 규제 관할권에 대해 '판례 충돌(circuit split)'을 야기했다. 이러한 규제 분열은 신생 부문의 성장과 법적 명확성에 상당한 걸림돌이 된다. 이와 동시에 비트코인(Bitcoin)의 최근 가격 움직임은 연방준비제도(Fed) 의장의 매파적 발언 이후 주춤하며, 디지털 자산 시장이 전통적인 거시경제 지표와 중앙은행 정책에 얼마나 깊이 통합되어 있고 또 취약한지를 여실히 보여준다. 이 세 가지 독특하지만 서로 연결된 이야기는 끊임없이 변화하는 글로벌 패러다임 속에서 기술적 우위, 규제 명확성, 그리고 시장 안정성을 추구하는 산업의 모습을 종합적으로 그려낸다. 블록체인 네트워크를 포함한 거의 모든 현대 디지털 시스템의 근본적인 보안은 공개키 암호화의 견고함에 기반한다. RSA와 타원곡선 암호화(ECC) 같은 알고리즘은 개인키와 디지털 서명을 보호함으로써 거래의 무결성과 디지털 자산의 소유권을 보장해왔다. 그러나 충분히 강력한 양자 컴퓨터의 이론적 출현은 이러한 암호화 기본 요소에 실존적 위협을 가한다. 특히 쇼어 알고리즘(Shor's algorithm)이 대규모 양자 컴퓨터에서 실행된다면, 큰 숫자를 효율적으로 인수분해하고 이산 로그 문제를 풀 수 있어 현재의 공개키 암호화를 무력화할 수 있다. 이러한 'Q-Day' 시나리오가 현실화되면 공격자들은 공개된 정보로부터 개인키를 유추해 디지털 지갑과 블록체인 원장의 불변성을 침해할 수 있다. 양자 컴퓨팅 능력의 정확한 시기는 여전히 불확실하지만, 잠재적인 파괴적 혼란 가능성은 리플이 XRP Ledger에 대해 보여준 선견지명처럼 선제적이고 장기적인 인프라 계획을 필수적으로 만든다. 이러한 기술적 당위성과 나란히, 디지털 자산 공간 내 혁신적인 금융 상품에 대한 규제 환경은 여전히 격전지다. 예측 시장은 미래 사건의 결과에 베팅할 수 있는 플랫폼으로, 정보 집약과 금융 파생상품의 흥미로운 교차점을 보여준다. 이러한 시장은 투명성과 효율성을 위해 블록체인 기술을 자주 활용하며, 다양한 실제 결과에 대한 가격 발견과 헤징을 위한 독특한 메커니즘을 제공한다. 하지만 이들의 분류는 중대한 도전 과제를 안고 있다. 과연 이들은 상품선물거래위원회(CFTC)와 같은 연방 규제 기관의 관할권에 속하는 합법적인 금융 '스왑(swaps)'일까, 아니면 주() 차원의 도박 규제를 받는 '스포츠 베팅'과 유사한 것일까? 이러한 정의의 모호성은 규제 공백과 관할권 분쟁을 야기하며, Kalshi와 관련된 현재 진행 중인 법적 분쟁이 이를 잘 보여준다. 통합된 규제 프레임워크의 부재는 혁신을 저해하고 법적 불

Some fortnights the complaints about AI come from people who barely use it. This one they came from the people who use it most. Scroll Hacker News over the past week — the forum where developers argue about their tools in unusual detail — and the grievances about AI coding assistants weren’t existential. Nobody was worried about the robots waking up. They were worried about their bill, their UI, and the effort of reading what the model just wrote. Quotes sourced from: Hacker News. Every quote below was located at its comment permalink and reproduced verbatim; each is listed with its username, the platform, and the date in the Sources section. As always, we quote experiences, not verdicts — a forum comment is one practitioner’s account, often mid-argument, and we’ve framed them as exactly that. What makes this batch worth reading isn’t volume; it’s specificity. These are checkable complaints. “Enshittified at a surprising clip”: the dark-pattern gripe The sharpest thread of the fortnight was about Cursor, the AI code editor, and it wasn’t about the quality of its completions. It was about the way the product behaves around you. A user posting as jmuguy , on 20 August, laid out a bill of particulars that will sound familiar to anyone who’s watched a beloved tool curdle: “Cursor isn’t covering itself in glory regardless. The flagship app is getting enshittified at a surprising clip. It constantly pops up and interrupts your work pushing new features, changes your model to whatever the latest Grok is without prompting, has this mystery meat UI that is constantly changing, pushes cloud agents in ways that are definitely designed to trick you. We’re actively looking at alternatives, I wouldn’t touch anything this company produces from here on out.” Set aside the verdict at the end — that’s one person’s conclusion, not ours — and look at the specifics, because they’re the kind you can check: interruptions pushing new features, a model silently swapped to Grok, a UI that ke

Twenty-one bytes. That is the entire attack. A file smaller than a URL, with four zero bytes sitting at exactly the right offset, crashes any FFmpeg-based application that opens it and reads a packet. Not memory corruption, not some exotic heap trick. A division by zero, in code that has been shipping for years, in one of the most fuzzed codebases on the planet. The person who found it, Darío Clavijo, did not write the fuzzer by hand. He built it with AI assistance, the way a growing number of security researchers now work, and posted the result on Hacker News this week under a title that got my attention immediately: "We found a division by zero bug in FFmpeg with a vibecoded fuzzer." The thread climbed past 250 points with hundreds of comments, and the debate underneath it is the real story: AI has been writing application code for two years, but AI writing the tester changes the economics of finding bugs in ways most teams have not priced in yet. Full disclosure before I go further. I am not a C security researcher. I run my own AI agent infrastructure and I write Java for a living. What I did for this article is what I would want you to do: I cloned the fuzzer's public repo, read its findings documents, tried to reproduce the crash on my own Ubuntu box, and studied the harness code line by line. Everything below is sourced from the public FFmpeg issue, the repo, and my own experiment, with the one place my results diverged clearly marked. What the fuzzer actually found The bug lives in libavformat/vpk.c , the demuxer for Sony PS2 VPK audio files, a container format almost nobody has heard of. That obscurity is exactly the point. In issue #24290 on the FFmpeg tracker , the crash chain reads like this: The probe matches. FFmpeg's format detection sees the VPK magic bytes and assigns the VPK demuxer. The header parses. vpk_read_header reads a 24-byte header. The crafted input sets the channel count, nb_channels , to zero at bytes 14 through 17. The header code does

If you searched for a klogg alternative , you probably already know klogg is good. It is fast, it is free, it is open source, and it runs on Windows, macOS and Linux. Most people who go looking for something else are not unhappy with klogg as a viewer. They are unhappy with one specific moment in their day: Opening the file again. You investigated a 48GB log yesterday. You closed it. This morning your colleague asks about a different error, and you have to wait through the whole index build a second time. On a USB HDD that is nine minutes of staring at a progress bar — and while it builds, klogg only shows you the beginning of the file. That is the problem this article is about. Below is a measured comparison on a real 47.73GB file, including the rows where klogg wins . The test File OpenStreetMap Japan japan-latest.osm — 47.73 GB, 892,239,125 lines Machine MacBook Air / Apple M4 (10 cores) / 32GB RAM Storage (measured with dd ) USB HDD 0.10 GB/s ／ USB SSD 0.41 GB/s ／ Internal SSD 3.29 GB/s Versions klogg 24.11.0 ／ UwView Pro Search hit counts were verified to match exactly across klogg, UwView Pro, and a direct search of the raw file — so we know both tools are answering the same question. The numbers klogg 24.11.0 UwView Pro Ratio First open HDD ~9 min ／ USB SSD ~110 s ／ Internal SSD ~15 s — every time HDD 10.6 min ／ USB SSD 138.5 s ／ Internal SSD 23.3 s — first time only klogg wins Reopening Same as the first open (re-indexes every time) 0.01–0.07 s ~1,250–50,000x Search, literal "Tokyo" ~585 s ／ 120–135 s ／ 15–20 s 74.8 s ／ 14.3 s ／ 5.1 s ~7.8x ／ ~9x ／ 3–4x Search, regex "Tok[yi]o" ≈ literal (I/O bound, pattern-independent) 29.8 s (USB SSD) ／ 11.0 s (Internal SSD) ~4.4x ／ ~1.5x Disk used to keep the file 48 GB (original required) 5.3 GB (original can be deleted) 1/9 Two things are worth saying plainly. klogg opens the file faster the first time. UwView Pro is slower on the first open because it is building a compressed cache while it reads. That is a real cost a

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This article is about running a hand-written Gemma 4 port in pure JAX on three different accelerators, and about the two places the abstraction leaks. The code is here: github.com/xbill9/gemma4-dev What is this project trying to Do? This project aims to serve one Gemma 4 checkpoint from one JAX port across every accelerator I can rent, and to find out — by measurement, not by reading docs — which parts of "it's just JAX" are true. The port lives in ports/gemma4/ and is driven by a generation loop behind an OpenAI-compatible server. No PyTorch, no vLLM, no torch_xla . The same code runs on Cloud TPU v5e and v6e, and on an NVIDIA T4G attached to an AWS Graviton2 host. "Pure JAX" is the whole experiment. If the port is really portable, the only thing that should change between those rigs is a config file. It mostly is. Two things are not, and they are the interesting part. Gemma 4 E2B is not a stock transformer Any port has to carry four irregularities, and none of them are optional: Two attention geometries. Sliding layers use head_dim=256 , global layers use 512 . Most inference stacks assume one head dimension per model. 8:1 MQA , so the KV budget is nothing like the parameter count would suggest. A KV-share map that collapses 35 layers onto 15 caches . A 512-slot sliding ring , plus per-layer embeddings (PLE) held in a 4.70 GB table that gets quantized to 4 bits on load. That first one is worth dwelling on, because it is what breaks other stacks. On the vLLM path, the heterogeneous head dims force the Triton attention backend: Gemma4 model has heterogeneous head dimensions {'sliding_attention': 256, 'full_attention': 512}. FA4 not available, forcing TRITON_ATTN backend. And on a Turing GPU that backend then asks for shared memory the hardware does not have: triton.runtime.errors.OutOfResources: out of resource: shared memory, Required: 98304, Hardware limit: 65536 JAX never enters that conversation. Attention is ordinary XLA rather than a hand-tiled kernel, so ther

This article provides a step by step deployment guide for serving Google's Gemma 4 on an AWS EC2 G5g instance using pure JAX. The code is here: github.com/xbill9/gemma4-dev What is this project trying to Do? This project aims to serve a modern open model on the cheapest whole CUDA GPU AWS will rent you, and to measure honestly what that costs. Aren't You Using The Wrong GPU? Probably! The T4G is a Turing chip from 2018. It has no bfloat16 and no fp8. But it is cheap, it is available when nothing else is, and it is attached to a Graviton2 host — which makes G5g the rare hardware axis that almost nothing in the ML ecosystem targets: aarch64 and CUDA together . So let's give pure JAX a shot on G5g! AWS EC2 G5g G5g instances pair an AWS Graviton2 (64-bit Arm) processor with NVIDIA T4G Tensor Core GPUs. At g5g.xlarge they are the cheapest EC2 instance carrying a whole NVIDIA GPU , and the only Arm-based GPU family AWS offers. Two GPU instances are cheaper per hour and neither can serve this model (us-east-1, Linux, on-demand, checked against the Pricing API on 2026-08-28): g6f.large at $0.2020 is genuinely NVIDIA and genuinely CUDA — but it is one eighth of a GPU with 3 GB , and the weights alone are 6.155 GB. The first g6f that fits is g6f.4xlarge at $0.9500, which is 1.7x this rig's g5g.2xlarge . g4ad.xlarge at $0.3785 carries an AMD Radeon Pro V520 — no CUDA at any price. Among whole NVIDIA GPUs, G5g is the floor: g5g.xlarge at $0.4200, and the next one up is g4dn.xlarge at $0.5260. More information is available here: https://aws.amazon.com/ec2/instance-types/g5g/ The default in this rig is g5g.2xlarge — 1 GPU, 8 vCPU, 16 GiB RAM. Note- the T4G reports 15,360 MiB of device memory, not the nominal 16 GB. Budget against the measured number. Gemma 4 Gemma is Google's family of open models built from the same research as Gemini. This rig serves google/gemma-4-E2B-it , the instruction-tuned reference release. JAX JAX is Google's array computing library — NumPy semantics, c

LINE published an official MCP server for its Messaging API, which means an AI agent can now drive a LINE Official Account directly — sending messages, broadcasting promotions, and pushing Flex Message cards without writing any API code. I set it up with Codex and worked through every capability the server exposes, from creating a fresh account to delivering a message to a real phone. This guide is the result: a complete walkthrough, and an honest account of the three places where the documentation and reality diverge. Key takeaways MCP is agent-agnostic. The same LINE server works with Codex, Claude Desktop, and Cline — only the config file format changes, from TOML to JSON. Codex stores MCP config in TOML , at ~/.codex/config.toml . Most guides assume the JSON format used by Claude Desktop, which is the single most common setup mistake. Verified account and API-capable account are different things. A free account can use the Messaging API, but get_follower_ids returns 403 Forbidden until the account is verified or on a premium plan. Official security advice can conflict with official features. LINE's example config disables npm install scripts, which also prevents the headless browser that the rich menu tool depends on from being installed. Agents have habits. Codex is a coding agent first: asked in natural language to build a rich menu, it wrote a Node script instead of calling the MCP tool. Naming the tool explicitly in the prompt fixes it. Broadcasts cannot be recalled. Set default_tools_approval_mode = "writes" so the agent asks before any send. Every screenshot comes from the actual working setup, including the errors. The article is available in both English and Thai. Devlycan - Technology & Programming Insights Devlycan - Technology, programming, AI, lifestyle, and future trends—simple insights for the new digital generation. devlycan.com