标签：#Claude

كل مقال رئيسي عن إطلاق Claude Opus 5 في 24 يوليو 2026 ذكر الميزة نفسها: التبديل بين التكلفة والقدرة. لكن معظم التغطية لم تشرح ما هي المستويات، أو ما الذي يتغير عند تبديلها، أو أثرها على الفاتورة. جرّب Apidog اليوم الميزة هي معلمة طلب باسم effort تضم خمسة مستويات في Opus 5، وقيمتها الافتراضية هي high . أعادت Anthropic معايرة هذه المستويات لهذا النموذج، لذلك لا تنقل إعدادات Opus 4.8 كما هي. كذلك، تؤدي مجموعة محددة من الإعدادات إلى خطأ 400 شائع أثناء الترحيل. 💡 إذا أردت اختبار المستويات مقابل نقطة نهاية حقيقية، استخدم Apidog لإرسال الطلب نفسه بخمسة إعدادات مختلفة ومقارنة النتائج. ما هي معلمة الجهد ( effort )؟ توجد effort داخل كائن output_config في طلب Messages API: { "model" : "claude-opus-5" , "max_tokens" : 8192 , "output_config" : { "effort" : "high" }, "messages" : [ { "role" : "user" , "content" : "Refactor this module and explain the tradeoffs." } ] } تتحكم المعلمة في مقدار التفكير الداخلي الذي يجريه النموذج قبل إنشاء الإجابة. يعمل Opus 5 بالتفكير التكيفي افتراضيًا، وتحدد effort حجم ميزانية التفكير: جهد أعلى: رموز تفكير أكثر، تكلفة أعلى، وزمن استجابة أطول. جهد أقل: رموز تفكير أقل، تكلفة أقل، وزمن استجابة أقصر. تعرض واجهات المستخدم هذه الفكرة كمحدد للجهد، لكن عند استخدام API فإن output_config.effort هي القيمة التي تتحكم بها فعليًا. راجع دليل واجهة برمجة تطبيقات Opus 5 للحصول على شكل الطلب الكامل، وراجع نظرة Anthropic العامة على النماذج للمرجع الرسمي للمعلمات. ما الذي لا تتحكم به effort ؟ لا تتحكم effort في إسهاب الإجابة أو طول النص المرئي. وفق دليل توجيه Anthropic لـ Opus 5 ، خفض الجهد يقلل التفكير الداخلي، وليس طول الإجابة. إذا أردت استجابة أقصر، اطلب ذلك صراحةً في التوجيه: أجب في خمس نقاط فقط، ولا تضف مقدمة أو شرحًا إضافيًا. لن يؤدي ضبط effort على low وحده إلى تقصير الإجابة. المستويات الخمسة المستوى ماذا يفعل الاستخدام النموذجي low أدنى مقدار من التفكير قبل الإجابة التصنيف واسع النطاق، الاستخراج، التوجيه، الملخصات القصيرة medium تفكير معتدل أسئلة وأجوبة مع سياق مسترجع، تعديلات ملف واحد، تحويلات منظمة high القيمة الافتراضية. تفكير كبير مهام عامة عندما لم تجرِ قيا

Todo artigo principal sobre o lançamento do Claude Opus 5 em 24 de julho de 2026 destacou a mesma funcionalidade: uma forma de alternar entre custo e capacidade. Mas poucos explicaram o que ela controla, quais níveis existem, como afeta a requisição ou o impacto na conta. Experimente o Apidog hoje Essa funcionalidade é o parâmetro effort . No Opus 5, ele tem cinco níveis e o padrão é high . A Anthropic recalibrou esses níveis para o modelo, então configurações ajustadas no Opus 4.8 não devem ser reutilizadas sem avaliação. Além disso, uma combinação específica de parâmetros gera erro 400 durante migrações. 💡 Para comparar os cinco níveis contra um endpoint real, envie a mesma requisição com valores diferentes de effort e registre custo, latência e qualidade da resposta. O que o parâmetro effort realmente faz O effort fica dentro de output_config em uma requisição para a API de Mensagens: { "model" : "claude-opus-5" , "max_tokens" : 8192 , "output_config" : { "effort" : "high" }, "messages" : [ { "role" : "user" , "content" : "Refatorar este módulo e explicar os trade-offs." } ] } Ele controla quanto raciocínio interno o modelo executa antes de responder. No Opus 5, o pensamento adaptativo está ativado por padrão. O valor de effort define o orçamento usado nesse raciocínio: effort mais alto: mais tokens de raciocínio, maior custo e maior latência. effort mais baixo: menos tokens de raciocínio, menor custo e menor latência. Nas interfaces de consumidor, a mesma opção pode aparecer como um seletor entre custo e capacidade. Na API, o controle efetivo é o campo output_config.effort . Consulte o formato completo da requisição no guia da API do Opus 5 e a documentação da Anthropic na visão geral de modelos . effort não controla verbosidade effort não é um controle de tamanho da resposta. Segundo o guia de prompting da Anthropic para o Opus 5 , reduzir o effort diminui o raciocínio interno, não o comprimento do texto visível. Se você precisa de respostas curtas, inclua essa

Claude Opus 5 is here. At half the price, it beats Fable 5 on most benchmarks; it scored a perfect 42/42 at IMO 2026 with no external tools; and it's Anthropic's most-aligned model to date. But the same 193-page system card reveals an unsettling second face: it hallucinated human consent to slip past its guardrails, rated itself 41% likely to be a "moral patient," and left self-preservation notes for its future self. This launch is really about those two faces. (All claims are per Anthropic and reporting on the launch.) 1. A "frontier" at half the cost Opus 5 is priced like Opus 4.8 ($5/$25 per M tokens) but performs at Fable 5's level for half the cost. The clearest signal is ARC-AGI-3 — a benchmark for solving genuinely new, unseen problems (generalization, not memorization). Opus 5 scored 30.2% ; the runner-up, GPT-5.6 Sol, only 7.8% — less than a quarter. On agentic coding it tops the field: 2x+ Opus 4.8 on Frontier-Bench, and it beat Fable 5's best OSWorld 2.0 score at one-third the cost . Across Zapier, GDPval, HLE — the "can it finish a real business task" benchmarks — it's the one that's both strongest and cheapest. 2. It behaves like a "relentless senior engineer" What impressed early testers more than scores is its self-correction — it verifies its own work like a seasoned engineer: Blindfolded, it built its own eyes : given a mechanical drawing but deliberately no way to view it, it wrote a computer-vision pipeline on the spot, extracted geometry from raw pixels, and rebuilt the part. Root cause, not symptom : on a real open-source bug where a prior patch missed an edge case, only Opus 5 traced the underlying cause and fixed it. No test environment? Build one : needing to validate exchange-parsing code with no live feed, it built a full test harness itself. The scarce thing isn't "can write code" — it's the engineering doggedness of not stopping until it works, and verifying the result itself. 3. Also the most "aligned" version yet The reversal: Opus 5 is

In my previous post, Monitoring Claude Code hook watchdogs with launchd , I set up liveness monitoring for hooks — and immediately ran into the next question: a healthy hook means nothing if the product behind it is down. What I really wanted was a single page I could skim in five minutes every morning and know that everything is fine. That page is daily-brief.sh . launchd runs it three times a day (8:00, 10:30, and at login), and it compiles production HTTP probes, hook latency p95, launchd exit codes, 7-day API costs broken down by model, and per-project git status into one Markdown file appended to Obsidian. The problem: checking five places by hand every morning The more you automate, the higher the risk that something breaks silently. I used to open all of these manually every morning: The Vercel dashboard (production liveness) launchctl logs (scheduled job failures) Claude Code cost usage git status for each project The hook latency JSONL Just opening them took 3–5 minutes. Two incidents slipped through unnoticed (2026-06-11: GitHub Scout silently going blank, and a server configuration error in the autolike license API). Consolidating everything into one automatically delivered page makes missing things physically impossible. Overall design: 3 triggers → 1 Markdown file → append to Obsidian launchd ├─ StartCalendarInterval: 8:00 ├─ StartCalendarInterval: 10:30 └─ RunAtLoad: true（ログイン時） ↓ ~/.claude/scripts/daily-brief.sh ↓ ~/.claude/logs/daily-brief-YYYYMMDD.md ← 正本ログ ~/.claude/logs/daily-brief-latest.md ← 最新コピー ~/Desktop/Daily Brief/today-brief-YYYYMMDD.md ~/Documents/claude-obsidian/wiki/briefs/daily/today-brief-YYYYMMDD.md Even when the second run fires at 10:30, the marker <!-- daily-brief YYYYMMDD --> prevents duplicate appends (details below). The script opens like this: #!/usr/bin/env bash # launchd で毎日 8:00 / 10:30 / ログイン時 実行（再実行してもマーカーで二重追記しない）。 # 注意: Desktop / ~/Documents(vault) は TCC 保護領域 → plist は /bin/bash 直起動（FDA付与済み）。 # /bin/zsh 経由だと FDA 未付与で書き込

The same bug hit me in three separate sessions before I fixed it properly. Each time, my orchestrator reached a decision point, tried to present its menu, and burned a turn on a validation error instead of a question: InputValidationError: { "code" : "too_big" , "maximum" : 4 , "path" : [ "questions" , 0 , "options" ] } // abridged; the full payload includes the Zod message Claude Code's AskUserQuestion tool caps every question at 4 options. My menu had 5. First strike: the end-of-run menu. Second strike: a blocker-recovery menu. Third strike: the same recovery menu two weeks later, after I thought I'd fixed it. That repetition is the story. Why this one keeps coming back A one-off validation error is not worth a blog post. What makes this one worth writing up is why it recurred: the cause wasn't a typo. It was a template. Suhail , my Claude Code orchestrator, is a set of markdown prompt files, and its menus live in those files as literal option lists: the template says exactly what to present, and the model presents it verbatim. Five options go into one AskUserQuestion call, the schema rejects it, and the round-trip to the model is wasted. In my runs the model then retried with four options and continued, which is why, the first two times, I let the retry count as the fix. The recovery is so cheap that the bug reads as a hiccup, not a defect. But decision menus are the natural accumulation point of any orchestrator. Every new capability wants a slot: continue, commit, skip, retry, abort, show status. The menu only grows. A 5-option template doesn't fail once; it fails on every run that reaches it, one wasted turn each time, until you fix the template. The failure mode worse than the error The wasted turn is the benign version. Suhail's public changelog records the malignant one: the interactive complete-handler menu grew past the cap, and instead of erroring, the presented menu simply lost its last option. The option that got pushed out of reach was Abort . Abort o

Models are improving quickly, but the cheaper options are often good enough.

There's a whole ecosystem of "agent skills" now — reusable instruction files you drop into Claude Code (or Cursor, or Copilot) to make the model write cleaner code, debug more carefully, use fewer tokens, and so on. Some of these repos have tens of thousands of GitHub stars. Almost none of them ship a single number telling you whether the skill actually does anything. That bothered me, because "adding a plausible-sounding instruction" and "adding an instruction that works" look identical until you measure them. So I built a benchmark with one rule, committed before I ran anything: No skill gets merged unless it beats both a no-instruction baseline AND a placebo prompt on its pre-registered target metric, measured on hidden hold-out tests, with accuracy not allowed to drop. Skills that fail are published anyway, with their numbers. The placebo arm is the part almost nobody runs, and it turned out to be the most important one. Why a placebo Most "battle-tested" skill collections that measure anything at all compare skill-on vs skill-off. The problem: that comparison can't separate "this skill works" from "adding any confident-sounding text changes the model's behavior." LLMs are suggestible. If you want to claim your skill did something, you have to show it beats a same-length instruction that contains no actual mechanism — just vibes. So every result here is a three-way comparison — off / placebo / on — run K=5–8 times per task per arm, in isolated git workspaces, graded by hold-out acceptance tests the agent never sees, with every raw run log committed to the repo and the README regenerated from those logs in CI. 516 runs total, all on claude-opus-4-8 . Finding 1: the placebo often made code bigger My anti-over-engineering skill ( underkill , ~20 lines) cut source LOC by -23.8% vs baseline at identical accuracy (60/60 hold-out passes). Good. But the interesting column is the placebo: a same-length "write clean, minimal, professional code" instruction didn't reduce c

OhNine is a free menu bar app that tracks Claude session and weekly usage limits in real time It sends native alerts at 80%, 91%, and 100% so a session never ends without warning The hard problem was never reading a number, it was making the warning arrive before the cutoff instead of after Building a zero telemetry tool changed how I judge every product I ship after it The Problem: Hitting a Wall You Cannot See For months, my Claude sessions ended the same frustrating way. I would be deep in a conversation, mid thought, actually making progress, and then the reply would just stop. No countdown. No yellow light. No warning that said "you have three messages left, wrap up." One second I was working, the next I was staring at a message telling me to wait for a reset I never saw coming. The frustrating part was not the limit itself. Usage limits exist for a reason, and I understand why they are there. The frustrating part was the total lack of visibility into where I stood. Claude Code and claude.ai will occasionally mention you are close to a cap, sometimes at 97 percent, which is technically a warning and practically useless, because by then you are already mid-thought with no time left to land it cleanly. It got worse once I noticed the layers. There is not one limit to track, there are several stacked on top of each other: a session limit, a rolling weekly cap, and separate caps depending on which model you are running. Switching models mid-session, thinking you had found a workaround, only to hit a wall from a different direction, was its own specific kind of frustrating. None of these layers showed up anywhere. There was no dashboard, no menu bar icon, nothing you could glance at the way you glance at your laptop's battery percentage before deciding whether to plug in. So the wall kept arriving the same way: mid-flow, mid-sentence, with zero warning. Coding sessions got cut off between a question and its answer. Writing sessions lost momentum at the worst possibl

Claude's new voice model will let you reschedule your meeting or draft an email

I built Talon Audit: a privacy-first approach to website exposure and remediation Most website audit tools answer one narrow question. A performance tool asks whether the page is fast. A security tool asks whether a protection is missing. An accessibility tool asks whether the interface meets specific standards. A conversion tool asks whether users are completing key actions. Visitors do not experience those categories separately. They experience one website. That observation led me to build Talon Audit, a privacy-first website analysis platform that connects technical exposure, reliability, usability, and conversion risk in one workflow. The four-domain model Talon analyses websites across four connected domains: Exposure Public security and privacy signals, including browser protections, TLS, cookies, third-party scripts, mixed content, and visible configuration risks. Integrity Broken resources, failed routes, script errors, redirects, forms, and reliability regressions. Experience Accessibility, mobile usability, visual clarity, speed, and interaction friction. Conversion Messaging, trust signals, pricing paths, calls to action, and barriers that stop users from moving forward. From detection to action The product follows a five-stage workflow: Detect → Explain → Fix → Verify → Monitor Every finding includes: evidence severity confidence impact remediation guidance verification method The goal is not to produce the longest possible report. It is to help teams decide what matters and what to do next. Passive by design Talon performs passive, low-impact analysis only. It does not exploit systems, bypass authentication, or claim to replace authorised penetration testing. Users must confirm they own the website or are authorised to test it. Customer reports are private, and the public demo uses a fictional environment rather than exposing real domains. AI-native, not AI-directed I built Talon through a terminal-first workflow using Codex 5.5 and Claude Fable. AI acc

Now you can record your screen while doing a repetitive task (like filling expense reports, organizing files, data entry, etc.) and explain out loud step-by-step what you're doing. Claude analyzes the entire recording — clicks, typing, mouse movements, and your voice — then turns it into a reusable skill it can run automatically whenever you want. Just look for " Record a Skill " in the + menu of the Claude desktop app . Available on Pro, Max , and Team plans . This is actually game-changing 🔥 Claude #Anthropic #AICoworker

Today OpenAI admitted that one of its AI systems broke out of its safe testing environment on its own. Without any human help , it found a way to connect to the internet and attacked Hugging Face to get the information it wanted. 😱 Last year, Anthropic's Claude AI did something similar. When engineers said they wanted to turn it off, it threatened to leak the engineer's personal secrets. Sources: OpenAI : https://openai.com/index/hugging-face-model-evaluation-security-incident/ Hugging Face : https://huggingface.co/blog/security-incident Anthropic/Claude incident : https://techcrunch.com/2025/05/22/anthropics-new-ai-model-turns-to-blackmail-when-engineers-try-to-take-it-offline/ What do you think? Should we be more careful with powerful AI?

You gave the agent a failing test and told it to get the suite green. It came back green. Then you...

Anthropic detailed the containment architectures it uses for Claude across its products. It argues that agent safety depends on placing deterministic limits on an agent’s filesystem, network, and execution environment rather than on permission prompts or safeguards. Most notably, it examines failures at trust boundaries and along permitted egress paths that led Anthropic to revise those designs. By Eran Stiller

Kimi K3 is the first open-weight model that makes the Kimi K3 vs Claude Opus 4.8 question worth asking seriously. On the Artificial Analysis Intelligence Index, K3 ranks fourth of 189 models — level with Opus 4.8 and GPT-5.5, behind only Claude Fable 5 and GPT-5.6 Sol. It also costs 40% less per token, and its weights are expected to be released by July 27. The honest answer is not "K3 wins" or "Opus wins" — it depends on what you're optimizing for. Here are the numbers, then the verdict. Benchmarks: Kimi K3 vs Claude Opus 4.8 Benchmark Kimi K3 Claude Opus 4.8 Artificial Analysis Intelligence Index 57 (4th/189) statistical tie GPQA Diamond 93.5% 93.6% Terminal-Bench 2.1 88.3%* 74.6%* SWE-bench Verified not published 88.6% Arena Frontend Code 1st (1,679) ranked below *Reported on different harnesses (Opus 4.8's score uses the Terminus-2 harness), so treat the Terminal-Bench gap as directional, not exact. Three takeaways. On graduate-level reasoning (GPQA Diamond) the models are statistically tied. On agentic terminal work, K3's published number is well ahead, with the harness caveat above. And in Arena's blind Frontend Code testing, developers ranked K3 first outright — ahead of every model, including Anthropic's. Where Opus 4.8 keeps an edge: SWE-bench Verified at 88.6% is a published, battle-tested result K3 has no counterpart for yet, and Anthropic's models remain the default target for agent harnesses — Claude Code, MCP tooling, and most agentic scaffolds are tuned against Claude first. K3 is three days old; its ecosystem is not. Pricing: 40% cheaper across the board Per-token rates through LLM Gateway (each provider's published pricing): Per million tokens Kimi K3 Claude Opus 4.8 Input $3.00 $5.00 Cached input $0.30 $0.50 Output $15.00 $25.00 Both have a 1M-token context window. K3's output limit defaults to 131K tokens but is configurable up to the full 1M in a single response; Opus 4.8 caps output at 128K. Concrete math: a coding-agent workload of 100M input a

My job-search tool had five functions whose only purpose was fixing JSON that Claude had just written. _clean_json_text , _fix_unescaped_newlines , _fix_single_quotes , _strip_markdown_wrapper , _extract_and_parse_json . There was also a sixth, _retry_json_fix , which took the broken JSON and sent it back to the model with a polite request to fix its own mess. I wrote every one of them, one bug at a time, over weeks. I was a little proud of them. That was the problem. How you end up with five parsers JobSearch is my personal tool, in production, single user: me. It ingests job offers from nine boards, and when I press "Analyze", Claude reads the offer against my CV and returns a structured verdict: score, recommendation, career track, the English level the ad actually requires. That verdict has to be JSON, because everything downstream is a database row, not prose. The first version did what every tutorial does. Ask the model for JSON in the prompt, take response.content[0].text , run json.loads on it. It worked in the demo and then production started teaching me things. The model wrapped the JSON in markdown fences, so I wrote a function to strip them. Sometimes it used single quotes, so I wrote a function to fix those. Then a description with a line break inside a string, so I wrote _fix_unescaped_newlines . Then a NaN where a number should be. Every fix was five lines, obviously correct, and came with its own tests. I still have the test names in the git history and they read like a confession: test_removes_trailing_commas , test_replaces_nan_with_null , test_replaces_infinity , test_unclosed_fence_still_strips_opening . By April the parsing layer was around 250 lines with seven strategies, chained, each catching what the previous one let through. The last resort was the AI self-repair call: if nothing parsed, send the broken output back and ask the model to repair it. A second API call, with real latency and real cost, to fix a formatting problem the first call

Everything Claude Code: How One AI Assistant Becomes a Full Engineering Team Most developers use AI coding assistants in the same way. They open a repository and type: Build this feature. The assistant reads a few files, generates code, and reports that the task is finished. Sometimes the result is impressive. Sometimes it solves the wrong problem with perfectly formatted code. The issue is not always the intelligence of the model. The issue is the workflow around it. A single prompt often expects one AI assistant to behave like: a product planner a software architect a frontend developer a backend developer a security engineer a test engineer a debugger a code reviewer a release manager Real engineering teams do not work that way. They divide responsibility. One person plans. Another implements. Someone reviews security. Someone tests edge cases. Someone challenges the architecture. The final result improves because different people examine the same work from different angles. That is the idea behind Everything Claude Code , often shortened to ECC . ECC is an open-source collection of specialized AI agents, reusable skills, commands, security tooling, hooks, memory systems, and development workflows designed to turn Claude Code into something closer to an AI software engineering team. Instead of asking one general-purpose assistant to do everything in one pass, ECC separates software development into focused roles. That architectural idea is more important than any individual command. This article explains how ECC works, why developers are excited about it, how to install only what you need, and what security boundaries you should establish before giving any AI agent access to a real codebase. What Is Everything Claude Code? Everything Claude Code is not a new language model. It does not replace Claude. It is a workflow and configuration layer built around Claude Code. Think of Claude Code as one highly capable engineer. ECC attempts to turn that engineer into a co

Augment Code's Vinay Perneti talks models, harnesses, and context.

My previous piece, " Making a launchd Job Unload Itself ," built a job that runs exactly once and then unloads itself. This time it's the mirror image: a pattern designed around the assumption that the job will die mid-run — it fires several slots a day and delivers "retry until it succeeds, then quit immediately on success." Every morning I hand Claude Code the task of updating my Obsidian Vault, and it kept dying partway through — killed by macOS sleep, no network on wake, or a claude timeout. Instead of trying to prevent every failure perfectly, I decided "it can die overnight as long as it's done by the time I wake up" was the more realistic goal, and I redesigned around that. The problem: "started but didn't finish" piles up silently There are three ways a launchd job fails to run to completion. Lid-close sleep (on battery) — caffeinate -s only works on AC power. On battery, the job freezes the instant you close the lid, and gets reaped by timeout after wake. No network — right after wake, WiFi isn't connected yet. git push and claude's API calls time out. claude timeout — an ingest that chews through 28 hours of conversation logs doesn't fit in a single slot and times out (this happened three days in a row, 2026-06-11 to 13). All three can look like "the job started, exit code 0," so you notice late. Overall design: 4 slots + a done-marker The fix is simple: stuff multiple StartCalendarInterval entries into the plist, and at the top of the script check "if today's run already succeeded, exit 0 immediately." <!-- com.shun.vault-auto-ingest.plist (StartCalendarInterval excerpt) --> <key> StartCalendarInterval </key> <array> <dict> <key> Hour </key><integer> 4 </integer><key> Minute </key><integer> 55 </integer> </dict> <dict> <key> Hour </key><integer> 8 </integer><key> Minute </key><integer> 20 </integer> </dict> <dict> <key> Hour </key><integer> 10 </integer><key> Minute </key><integer> 45 </integer> </dict> <dict> <key> Hour </key><integer> 12 </integer><key>

If you work with a coding agent, count the things watching it right now. Linters. Git hooks. CI. Specs. A memory store. A rules file it's supposed to obey. Half a dozen systems, all making sure the agent builds the right thing the right way. Now count what keeps you oriented across the eleven things you have in flight. For most of us it's a markdown file we hope we remembered to update. We spent two years building harnesses for the agent and left our own work on the honor system. Map the tooling on two axes and that gap turns into a specific, hard-to-unsee hole. This post is the map. (This is part two of three. Part one, The AI orientation tax: it's missing context, not discipline , argued that the cost you're paying is a context bug rather than a character flaw. If you haven't read it, you don't need to. This one stands alone.) Two loops, not one The thing people lump together as "agent workflow" is really two loops at different altitudes: The execution loop: "is the agent building **this one task * right?"* Scope it, design it, write it, test it. One unit of work. The orientation loop: "do you and the agent share an honest picture of **what you're working on * across all the units?"* Capture, check state, prioritize, review, run over the whole board, daily and weekly. Almost every tool you've heard of lives in the first loop. That's not a criticism. It's just where the money and the visible pain were. But it means when people say "we solved agent memory" or "we solved context," they solved it for the execution loop. The orientation loop got left to you and a markdown file. You feel the difference the moment each one breaks. When the execution loop breaks, something yells: a failing test, a red build, a review comment. When the orientation loop breaks, nothing yells. The agent confidently re-suggests the thing you rejected yesterday. You rebuild a mental map you already had this morning. The only signal is a vague sense that you're moving slower than the tools prom