Show HN: Kanban-style Phase Board: plan → execute → verify → commit

Show HN (score: 5)
Found: August 01, 2025
ID: 635

Description

IDE/Editor
Show HN: Kanban-style Phase Board: plan → execute → verify → commit After months of feedback from devs juggling multiple chat tools just to break big tasks into smaller steps, we re‑imagined our workflow as a Kanban‑style Phase Board right inside your favourite IDE. The new Phase mode turns any large task into a clean sequence of PR‑sized phases you can review and commit one by one.

How it works

1. Describe the goal (Task Query) – In Phase mode, type a concise description of what you want to build or change. Example: “Add rate‑limit middleware and expose a /metrics endpoint.” Traycer treats this as the parent task. 2. Clarify intent (AI follow‑up) – Traycer may ask one or two quick questions (constraints, coding style). Answer them so the scope is crystal‑clear. 3. Auto‑generate the Phase Board – Traycer breaks the task into a sequential list of PR‑sized phases you can reorder, edit, or delete. 4. Open a phase & generate its plan – get a detailed file‑level plan: which files, functions, symbols, and tests will be touched. 5. Handoff to your coding agent – Hit Execute to send that plan straight to Cursor, Claude Code, or any agent you prefer. 6. Verify the diff – When your agent finishes, Traycer compares the diff to the plan and checks compatibility with upcoming phases, flagging any mismatches. 7. Review & commit (or tweak) – Approve and commit the phase, or adjust the plan and rerun. Then move on to the next phase.

Why it helps?

* True PR checkpoints – every phase is small enough to reason about and ship. * No runaway prompts – only the active phase is in context, so tokens stay low and results stay focused. * Tool-agnostic – Traycer plans and verifies; your coding agent writes code. * Fast course-correction – if something feels off, just edit that phase and re-run.

Try it out & share feedback

Install the Traycer extension (https://traycer.ai/installation), create a new task, and the Phase Board will appear. Add a few phases, run one through, and see how the PR‑sized checkpoints feel in practice. If you have suggestions that could make the flow smoother, drop them in the comments - every bit of feedback helps.

More from Show

Show HN: KeyEnv – CLI-first secrets manager for dev teams (Rust)

Show HN: KeyEnv – CLI-first secrets manager for dev teams (Rust) Hi HN,<p>I built KeyEnv because I was tired of the &quot;can you Slack me the Stripe key?&quot; workflow.<p><pre><code> The problem: My team&#x27;s secrets lived in a mix of Slack DMs, shared Google Docs, and .env files that definitely weren&#x27;t in .gitignore at some point. Enterprise tools like Vault required more DevOps time than we had. Doppler was close but felt heavier than we needed. What KeyEnv does: keyenv init # link project keyenv pull # sync secrets to local .env keyenv run -- npm start # inject secrets, run command That&#x27;s basically it. Secrets are encrypted client-side (AES-256-GCM) before leaving your machine. Zero-knowledge architecture—we can&#x27;t read your secrets even if we wanted to. Technical details: - Single Rust binary, no runtime dependencies - Works offline (cached secrets) - RBAC for teams (owner&#x2F;admin&#x2F;member&#x2F;viewer) - Service tokens for CI&#x2F;CD - Full audit trail Honest tradeoffs: - SaaS only, no self-hosted option - Fewer integrations than Doppler - If you need dynamic secrets or PKI, use Vault Pricing: Free tier (3 projects, 100 secrets), $12&#x2F;user&#x2F;month for teams. Would love feedback on the CLI UX and any rough edges. Happy to answer questions about the architecture. </code></pre> <a href="https:&#x2F;&#x2F;www.keyenv.dev" rel="nofollow">https:&#x2F;&#x2F;www.keyenv.dev</a>

Show HN: WebTerm – Browser-based terminal emulator

Show HN: WebTerm – Browser-based terminal emulator

Show HN: WebGPU React Renderer Using Vello

Show HN: WebGPU React Renderer Using Vello I&#x27;ve built a package to use Raph Levien&#x27;s Vello as a blazing fast 2D renderer for React on WebGPU. It uses WASM to hook into the Rust code

Show HN: On the edge of Apple Silicon memory speeds

Show HN: On the edge of Apple Silicon memory speeds I have developed open source CLI-tool for Apple Silicon macOS. It measures memory speeds in different ways and also latency. It can achieve up to 96-97% efficiency on read speed on M4 base what is advertised as 120GB&#x2F;s. All memory operations are in assembly.<p>I would really appreciate for results on different CPU&#x27;s how benchmark works on those. I have been able to test this on M1 and M4.<p>command : &#x27;memory_benchmark -non-cacheable -count 5 -output results.JSON&#x27; (close all applications before running)<p>This will generate JSON file where you find sections copy_gb_s, read_gb_s and write_gb_s statics.<p>Example M4 with 10 loops: &quot;copy_gb_s&quot;: { &quot;statistics&quot;: { &quot;average&quot;: 106.65421233311835, &quot;max&quot;: 106.70240696071005, &quot;median&quot;: 106.65069297260811, &quot;min&quot;: 106.6336774994254, &quot;p90&quot;: 106.66606919223108, &quot;p95&quot;: 106.68423807647056, &quot;p99&quot;: 106.69877318386216, &quot;stddev&quot;: 0.01930653530818627 }, &quot;values&quot;: [ 106.70240696071005, 106.66203166240008, 106.64410802226159, 106.65831409449595, 106.64148106986977, 106.6482935780762, 106.63974821679058, 106.65896986001393, 106.6336774994254, 106.65309236714002 ] }, &quot;read_gb_s&quot;: { &quot;statistics&quot;: { &quot;average&quot;: 115.83111228356601, &quot;max&quot;: 116.11098114619033, &quot;median&quot;: 115.84480882265643, &quot;min&quot;: 115.56959026587722, &quot;p90&quot;: 115.99667266786554, &quot;p95&quot;: 116.05382690702793, &quot;p99&quot;: 116.09955029835784, &quot;stddev&quot;: 0.1768243167963439 }, &quot;values&quot;: [ 115.79154681380165, 115.56959026587722, 115.60574235736468, 115.72112860271632, 115.72147129262802, 115.89807083151123, 115.95527337086908, 115.95334642887214, 115.98397172582945, 116.11098114619033 ] }, &quot;write_gb_s&quot;: { &quot;statistics&quot;: { &quot;average&quot;: 65.55966046805113, &quot;max&quot;: 65.59040040480241, &quot;median&quot;: 65.55933583741347, &quot;min&quot;: 65.50911885624045, &quot;p90&quot;: 65.5840272860955, &quot;p95&quot;: 65.58721384544896, &quot;p99&quot;: 65.58976309293172, &quot;stddev&quot;: 0.02388146120866979 },<p>Patterns benchmark also shows bit more of memory speeds. command: &#x27;memory_benchmark -patterns -non-cacheable -count 5 -output patterns.JSON&#x27;<p>Example M4 from 100 loops: &quot;sequential_forward&quot;: { &quot;bandwidth&quot;: { &quot;read_gb_s&quot;: { &quot;statistics&quot;: { &quot;average&quot;: 116.38363691482549, &quot;max&quot;: 116.61212708384109, &quot;median&quot;: 116.41264548721367, &quot;min&quot;: 115.449510036971, &quot;p90&quot;: 116.54143114134801, &quot;p95&quot;: 116.57314206456576, &quot;p99&quot;: 116.60095068065866, &quot;stddev&quot;: 0.17026641589059727 } } } }<p>&quot;strided_4096&quot;: { &quot;bandwidth&quot;: { &quot;read_gb_s&quot;: { &quot;statistics&quot;: { &quot;average&quot;: 26.460392735220456, &quot;max&quot;: 27.7722419653915, &quot;median&quot;: 26.457051473208285, &quot;min&quot;: 25.519925729459107, &quot;p90&quot;: 27.105171215736604, &quot;p95&quot;: 27.190715938337473, &quot;p99&quot;: 27.360449534513144, &quot;stddev&quot;: 0.4730857335572576 } } } }<p>&quot;random&quot;: { &quot;bandwidth&quot;: { &quot;read_gb_s&quot;: { &quot;statistics&quot;: { &quot;average&quot;: 26.71367836895143, &quot;max&quot;: 26.966820487564327, &quot;median&quot;: 26.69907406197067, &quot;min&quot;: 26.49374804466308, &quot;p90&quot;: 26.845236287807374, &quot;p95&quot;: 26.882004355057887, &quot;p99&quot;: 26.95742242818151, &quot;stddev&quot;: 0.09600564296001704 } } } }<p>Thank you for reading :)

No other tools from this source yet.