标签：#CLI

When a CLI becomes useful, someone eventually tries to automate it. That is where a progress bar can quietly become a problem. For a person, this kind of output is helpful: Translating markdown files 12/40 30% docs/intro.md It tells me that the command is alive, how far it has moved, and which file it is working on. But when another system starts reading that same output, the progress bar stops being only a user interface. It becomes an accidental contract. That was the real problem behind one of the changes in Co-op Translator v0.20.0. The release added a Rich-powered CLI progress UI, but it also added structured translation events. At first, those may look like two separate improvements. They are really two surfaces for the same state: Rich output gives a person something readable, while structured events give integrations something stable. This article is about three things: First, why console output is tempting to parse. Second, how Co-op Translator separated the Rich UI from the event stream. Third, why that separation matters for CLI, Python API, MCP, and product integrations such as Localizeflow. The problem appears when logs become state Console output is written for people. When I run a translation command, I want a quick answer to a few practical questions: Is the command still running? Which stage is active? Which file is being processed? How much work is left? Did anything fail? A progress bar is good for that. It compresses the state of the run into something I can scan quickly. But a product integration needs a different kind of information. Imagine Localizeflow running Co-op Translator as part of a larger workflow. It does not only need to know that text was printed. It needs durable state: Which translation job started Which target language is active Which stage is running Which file completed Which file failed How many items are done Whether the run succeeded If all of that only exists inside console text, the integration has to parse human language

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I was really looking forward to July 4, and not just because I love a poolside barbecue. This year the American holiday also marked a big symbolic deadline for US nuclear power. Last year the Trump administration set a goal to see three new microreactors achieve criticality, a technical milestone establishing that a reactor can…

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Go from an empty repository to a live custom domain with HTTPS in about 14 minutes, without manually editing a single DNS record. The post How GitHub Copilot enables zero DNS configuration for GitHub Pages appeared first on The GitHub Blog .

There are a lot of fast-growing AI startups, but some are growing even faster, they say.

Momentic, the company behind an AI-driven software testing platform, recently rearchitected its caching system to handle over 2 million queries per day across 20 billion total entries, while maintaining an average response latency of around 250 ms. This improvement was made possible by transitioning from PostgreSQL to the column-oriented database ClickHouse. By Sergio De Simone

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これは、Apidog が API テストおよび API ライフサイクル管理のためのコマンドラインツールである Apidog CLI をどのように開発したかを共有する全10回のシリーズです。順番に読むか、必要なテーマから直接参照してください。 今すぐApidogを試す タイトル 焦点 1 当社は126のMCPツールを構築しました。しかし、それはAgentにとって最良の解決策ではありません 問題の発見 2 なぜ当社は全く新しいApidog CLIを開発したのか アーキテクチャ開発 3 黄金律：CLIは事実を生成し、モデルは事実に従って行動する 核となる哲学 4 agentHints : CLIにAgentとの会話を教える 構造化出力 5 SKILL：運用経験をコードとして出荷する 運用経験 6 数字は嘘をつかない：ツール呼び出しは30%減、トークンは25%減 定量的結果 7 PRDからテストループまで：Apidog CLIによる完全なAgentワークフロー 実践的なチュートリアル 8 なぜCI/CD互換性がAgentツールにとって不可欠なのか DevOpsの視点 9 AIブランチ：AI Agentによるより安全なプロジェクト変更 セキュリティレイヤー 10 Spec-Firstは昨日。Skill-Firstへようこそ。 ビジョンと未来 当社は、MCPが最適化しない複雑なワークフロー、つまり検証ゲートと構造化された実行を伴うワークフローを処理するために、CLI + SKILLを構築しました。 MCPはその目的を果たし続けています CLI + SKILLに入る前に、前提を明確にします。 Apidog MCPは現在も利用可能で、メンテナンスされています。 MCPは、プロトコルに従って標準化されたツール接続を提供します。特に以下の用途に適しています。 シンプルで明確に定義された操作 MCPベースのワークフローを好むユーザー MCP準拠クライアントとのエコシステム統合 当社はMCPを置き換えたわけではありません。CLI + SKILLはMCPを補完するために構築されました。 MCPは ツール接続 に優れています。一方で、検証、読み戻し、実行確認を伴う多段階のR&Dワークフローでは、Agentに対して 実行可能なエンジニアリングプロセス を提供するほうが安定します。そこにCLI + SKILLが適合します。 タスクごとに使い分けると、次のようになります。 タスクタイプ 推奨されるアプローチ シンプルなツール呼び出し（例：エンドポイントの取得） MCPまたはCLI。どちらも機能します 多段階ワークフロー（例：テストの作成、検証、実行） CLI + SKILL。より良い体験になります CI/CD統合 CLI。ネイティブに適合します MCPエコシステム統合 MCP。プロトコル標準に適合します 古いCLI：最後にテストを実行する Apidog CLIは長年、APIテストを実行するためのコマンドラインのエントリポイントでした。 apidog run --project <projectId> --test-scenario <scenarioId> --environment <environmentId> この基盤は今も重要です。チームには以下を行うための信頼できる方法が必要です。 ターミナルからAPIテストを実行する CIパイプラインでレポートを生成する 自動化ワークフロー内で品質ゲートを維持する ただし、古いCLIの主な役割は テスト実行 でした。つまり、ワークフローの終盤で使われます。 設計 → ドキュメント化 → モック → デバッグ → テスト → [CLIがテストを実行] CLIは最後のステップでした。他の作業が完了したあとに、既存のテストを実行するためのものだったのです。 新しい要件：Agentにはより多くの操作が必要 API開発は変化しています。 AI Agentは現在、APIライフサイクルの複数段階に参加します。 段階 Agentの活動 API設計 PRDからエンドポイント定義を生成する テスト生成 API仕様からテストケースを作成する デバッグ 障害を分析し、修正案を提示する 移行 プロジェクト間でAPIを移動する メンテナンス API変更時にテストを更新する このようなワークフローでは、CLIは既存テストを最後に実行するだけでは不十分です。 Agentが安定して作業するには、CLI側で次の操作を提供する必要があります。 APIアセット（エンドポイント、スキーマ、環境）を読み取る テストアセット（テストケース、テストシナリオ）を作成または更新する 書き込み前に構造化された変更を検証する 変更をプロジェクトに書き戻す 実行結果を検証

When you delete files in your virtualbox VM in order to free up space on the host filesystem, this space is not automatically reclaimed. In order for the host system to see the changes you need to rewrite the free space with zeroes. Follow the below steps to perform this operation: Install zerofree package. It is needed to rewrite the free space with zeroes. Mount the filesystem as "readonly". This is needed for the tool to be able to perform it's task. If you're working with the "/", easiest way to mount it as readonly is to edit the kernel parameters. Edit /etc/default/grub . Find the GRUB_CMDLINE_LINUX_DEFAULT line. Add init=/bin/bash to it reboot Run zerofree -v /dev/sdX . This could run for some time, depending on the size of your disk. After it's done, run exec init to finish booting up. Shutdown the VM in order to be able to run the next command which requires a lock on the VDI volume. On the Windows host run VBoxManage.exe modifymedium "path\to\disk.vdi" --compact

Wildfire survivors call fire-prediction markets “morally reprehensible” and worry they could increase the risk of arson.

A new study has succeeded in mapping, on a global scale, the fungal network that supports plant life and helps regulate our planet’s climate.

I like neofetch / fastfetch , but I wanted one with a genuinely empty dependency graph — nothing pulled from crates.io. So I built purefetch : a small system-info fetcher written entirely in Rust using only std plus raw Linux syscalls. Disclosure up front: purefetch was built largely with AI assistance (Claude Code). I directed the design, and every change was reviewed and tested — including running it on four architectures under QEMU — but most of the code is AI-generated. I'd rather be honest about that than pretend otherwise. _,met$$$$$gg. ooonea@unicorn ,g$$$$$$$$$$$$$$$P. ────────────── ,g$$P" """Y$$.". OS Debian GNU/Linux 13.5 (trixie) x86_64 ,$$P' `$$$. Host ThinkPad P53 (20QQS0JD01) ',$$P ,ggs. `$$b: Kernel 6.12.94+deb13-amd64 `d$$' ,$P"' . $$$ Uptime 6 days, 15 hours, 30 mins $$P d$' , $$P Packages 2477 (dpkg), 1 (flatpak) $$: $$. - ,d$$' Shell zsh 5.9 $$; Y$b._ _,d$P' Display 1920x1080 (eDP-1) Y$$. `.`"Y$$$$P"' DE GNOME 48.7 `$$b "-.__ WM Mutter (Wayland) `Y$$ Terminal kitty 0.41.1 `Y$$. CPU Intel(R) Core(TM) i7-9850H @ 4.60 GHz `$$b. GPU Quadro RTX 3000 `Y$$b. Memory 15.28 GiB / 62.61 GiB (24%) `"Y$b._ Swap 0 B / 8.00 GiB (0%) `""" Disk (/) 8.52 GiB / 489.57 GiB (2%) Locale en_US.UTF-8 Battery 76% (Not charging) Zero dependencies, really No libc crate, no sysinfo , no nix , no color crate — nothing from crates.io. Almost everything is just reading and parsing /proc and /sys . The result is a single ~484 KiB binary that builds offline. The only things std can't do are statfs (disk usage) and ioctl (terminal size / tty check). Instead of pulling in a binding crate, those are issued as raw Linux syscalls via core::arch::asm! : #[cfg(target_arch = "x86_64" )] unsafe fn syscall3 ( n : usize , a1 : usize , a2 : usize , a3 : usize ) -> isize { let ret : isize ; core :: arch :: asm! ( "syscall" , inlateout ( "rax" ) n as isize => ret , in ( "rdi" ) a1 , in ( "rsi" ) a2 , in ( "rdx" ) a3 , out ( "rcx" ) _ , out ( "r11" ) _ , options ( nostack ), ); ret } Four arch

GitHub said this week that Copilot CLI, when it runs inside a GitHub Actions workflow, will accept the built-in GITHUB_TOKEN for authentication. Per the July 2 changelog, the previous path required creating and storing a personal access token. The operational read is small and precise: one fewer human-owned credential to mint, rotate and inherit. The exact scope of the change The changelog covers a narrow surface. It applies to Copilot CLI when invoked from a GitHub Actions workflow, and it swaps the required credential from a PAT to the workflow's ambient GITHUB_TOKEN . GitHub does not describe changes to how Copilot CLI authenticates outside Actions, and this post will not extrapolate to those contexts. If your Copilot CLI usage lives on a developer laptop or in another CI system, nothing in this announcement moves for you. Why the PAT was the wrong credential to leave in the loop A personal access token has almost none of the properties you would want from an automation credential. It does not expire on a job boundary. It carries a person's identity, not the workflow's. It sits in Actions secrets long enough to outlive the engineer who created it. And its scopes were chosen by that engineer, at that moment, often wider than the job actually needs. GITHUB_TOKEN is the opposite shape. Actions mints it at the start of a job, scopes it through the workflow's permissions: block, and revokes it when the job ends. If the token leaks, the window for abuse is the runtime of the job, not the years until somebody remembers to rotate it. When the person who wrote the workflow leaves, the pipeline does not silently break because a token expired with their account. For scripted Copilot CLI calls that had to be wrapped in a PAT, that is the whole win. The tool authenticates against the workflow instead of against a human. Wiring it up The workflow-side pattern is the same one every GITHUB_TOKEN -consuming step already follows: declare permissions: explicitly at the job level, k

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Introduction As data volumes continue to grow, running aggregation queries directly on raw datasets becomes increasingly expensive. Business dashboards, analytics platforms, and reporting systems often execute the same calculations repeatedly—such as total sales, daily active users, page views, or revenue trends. While ClickHouse® is designed to process analytical workloads at remarkable speed, repeatedly scanning billions of records still consumes valuable CPU, memory, and storage resources. This is where AggregatingMergeTree proves its value. Rather than calculating aggregates every time a query is executed, AggregatingMergeTree stores intermediate aggregation states that are merged automatically in the background. This approach allows analytical queries to read compact, pre-aggregated datasets, resulting in dramatically faster response times and reduced infrastructure costs. In this guide, you'll learn how AggregatingMergeTree works, why aggregate states matter, how to build an automated aggregation pipeline using Materialized Views, and when this engine is the right choice for your ClickHouse® workloads. What is AggregatingMergeTree? AggregatingMergeTree is a specialized ClickHouse® table engine designed to store aggregate function states instead of raw records. Unlike the standard MergeTree engine, which stores every inserted row, AggregatingMergeTree keeps partially aggregated values that ClickHouse combines during background merge operations. This significantly reduces the amount of data that must be processed when generating analytical reports. Because much of the computational work happens during data ingestion, dashboards and reporting applications can retrieve summarized information much more efficiently. Typical scenarios include: Sales reporting Website traffic analytics Financial summaries IoT sensor monitoring Business KPI dashboards Application observability metrics Why Use AggregatingMergeTree? Imagine an online marketplace processing millions of tr

GitHub Actions shows you one run at a time. Green check, red X, green check, green check, red X. You scroll the list, you re-run the flaky one, you move on. Nobody's asking the question that actually matters: is this getting better or worse? "I calculated how much my CI failures actually cost. Curious what your pipeline success rate looks like — has anyone else tracked the actual wasted compute time over time?" That's a real question from someone who did the math by hand and found their failures were burning a real chunk of their compute budget. The replies were the same story you'd expect: heavyweight CI platforms have their own dashboards for this, but nobody had a lightweight, local way to just... track it. So I built citrend : pull your GitHub Actions run history into a local file, get a trend. npx citrend sync --repo owner/name npx citrend report --repo owner/name What it actually shows you $ citrend report --repo acme/widgets acme/widgets — 812 run(s) (2 in progress) success rate: 87.4% (699/800 settled, 12 skipped) wasted runs: 101 (12.6%) total compute: 118h 42m wasted compute: 14h 6m weekly trend (oldest → newest): 2026-06-05 91.2% success, 8 wasted (58m) 2026-06-12 88.0% success, 11 wasted (1h 22m) 2026-06-19 79.4% success, 22 wasted (3h 8m) 2026-06-26 84.1% success, 15 wasted (2h 1m) That weekly column is the entire point. A single gh run list will never show you that week 3 was a cliff — you'd have to notice it got annoying to work in, which is a much slower and much less precise signal than a number going from 91% to 79%. How it works sync pulls your workflow run history from the GitHub REST API and caches it locally (deduped by run id, so you can run it on a schedule without piling up duplicates). report reads that cache — no network call — and computes: Success rate , over settled runs only (still-running runs don't count either way until they conclude, and skipped runs are excluded from the denominator since they're not a pass/fail outcome). "Wasted"

You write the postmortem. You file the action items. Everyone nods, the doc gets archived, and life moves on. Six months later, the exact same root cause takes down the exact same service — and nobody in the room remembers the first incident, let alone that its fix never actually shipped. "We use rootly to track this automatically. It flags when incidents have the same root cause as previous ones." That's a real answer from an SRE thread about this exact problem — and it's a paid, hosted feature of a full incident-management platform. Most teams don't have rootly or incident.io. What they have is a folder of markdown postmortems that nobody diffs against each other. So I built rootecho : a zero-dependency CLI that does the one useful thing those platforms do for this — flag when a new incident's root cause echoes a past one, and show you whether that past incident's action items ever actually got finished. How it works Each postmortem is one JSON record — free-text root_cause and/or curated root_cause_tags , plus action_items with a status: { "id" : "INC-2026-014" , "title" : "Payment webhook retries exhausted" , "root_cause" : "webhook retry queue misconfigured to drop after 3 attempts, no dead-letter fallback" , "root_cause_tags" : [ "webhook" , "retry-queue" , "dead-letter" , "config" ], "action_items" : [ { "id" : "AI-1" , "description" : "Add dead-letter queue for webhook retries" , "owner" : "alice" , "status" : "open" } ] } rootecho add records it and compares against your history: $ rootecho add inc-2026-014.json ⚠ root cause echo detected for "INC-2026-014": INC-2026-003 (2026-03-15) — 100% similar root cause Payment webhook retries exhausted ✓ Add retry backoff [done] ✗ Add monitoring alert for queue depth [open] — 93d overdue → 1 action item(s) from this past incident were never finished. recorded to .rootecho/history.jsonl That's the whole point of the tool in one output: not just "you've seen this before," but "and here's the fix that never happened." r

AI has made it a lot harder for tech companies like Amazon and Google to deliver on their net-zero pledges.