DY

Approved AI status text, but the handoff needs PowerPoint? Here's a copyable Markdown→PPTX recipe with a complete three-slide source and speaker note. Review the AI's facts and wording first. Pandoc converts the text; it doesn't approve the content. Get Pandoc's official installer for your computer: pandoc.org/installing.html Once pandoc runs in your terminal, save the following fictional example as UTF-8 plain text named deck_source, with no extension. Copy from the first # through the last bullet: # Launch review - Help-center draft is ready for editorial review. - Product screenshots still need approval. ::: notes Fictional example. Keep the launch date open until screenshots are approved. ::: # Open decision - Publish the text-only guide first. - Add screenshots after product review. # Next actions - Editor: approve the guide wording. - Product lead: clear screenshots for public use. - Release owner: schedule publication after approval. Run from the folder containing deck_source: pandoc -f markdown -t pptx --slide-level=1 deck_source -o deck_output.pptx Each # heading starts a slide. The notes block becomes speaker notes. No title metadata means no extra metadata title slide. For house styling, Pandoc's --reference-doc option uses a compatible PowerPoint reference file's layouts; the manual lists the required layout names. Pandoc is free/open source. PowerPoint or another compatible viewer has separate access/licensing conditions. Open the output to check text fit, layout and speaker notes before handing it over. Official recipe and reference-layout requirements: pandoc.org/MANUAL.html#slide…

Two AI skills to save for your next launch: one turns a rambling idea into a brief you can correct; the other shows where AI-simulated readers lose interest in a draft. Both live in Shubham Saboo's awesome-llm-apps, an open-source library spanning agent skills, multi-agent teams, document Q&A, voice and MCP apps. The useful move is to pick a resource for the job in front of you. 1. Thinking Out Loud — when the idea keeps changing as you explain it Its instructions require the agent to return an “echo brief” before acting: the mission, decisions already made, open questions, reversals and parked tangents. The model's inferences and guesses get their own section so you can correct them before approving work. That distinction matters: a follow-up question checks what the model doubts. An echo also exposes what it confidently misunderstood. Choose this when a long dictated message contains “actually, scrap that” or leaves terms like “the usual model” undefined. It works on the text you give the agent; it is not an audio recorder or transcription service. Source and skill: github.com/Shubhamsaboo/awes… 2. First Reader — when the draft is written, but you need another view of the reading experience This uses AI personas, including a skeptical skimmer. In full mode, simulated readers receive prose one passage at a time and log interest, confusion and quit points. A recall step works from their notes. The output includes a local page with comments beside the passages; the skill reports rather than rewrites. Quick read runs only the skim check. Choose this to inspect a launch post or landing-page narrative for places to investigate in revision. Its “next-day recall” is simulated: no day passes, and no human beta readers were recruited. The result cannot establish how real people will respond. Source and skill: github.com/Shubhamsaboo/awes… These are separate options, not a tested combined workflow. The source is openly downloadable under the repository's Apache-2.0 license. You still need a compatible coding-agent host; model usage may cost money, and the host/provider determines data handling. Local helper scripts and a local report page do not make the AI model offline or guarantee that your draft stays on your computer. Browse the wider library: github.com/Shubhamsaboo/awes…

Review a saved code diff with AI and keep a readable Markdown report plus JSON identifying skipped or truncated files—without adding a bot to your repository. PR-Agent offers plain-diff review for a useful first pilot: inspect one change and its coverage before deciding whether to connect a reviewer to pull requests. Use source commit 7cd2a66 after dependency/model setup, from its configured Python environment. These flags are verified against that source; packaged-release availability is unverified. python -m pr_agent.cli --diff-file changes_diff --output review_markdown --json-output review_json review Supply your own unified diff as changes_diff. The outputs are Markdown in review_markdown and JSON in review_json. Keep output paths different, and save old results before rerunning. JSON includes parsed review data, coverage, and token counts when available. Running inside the matching working tree supplies changed-file context; unavailable files get patch-only review. Plain-diff mode needs no hosting token or PR URL. Model access still requires either a configured local model or your inference provider's API key. Local execution alone doesn't mean offline inference. The code is MIT; model-provider and compute costs are separate. When choosing any AI reviewer, compare five things: • Context: what it sees beyond changed lines. • Selection: how candidate findings get filtered. • Authority: whether it can comment, edit or approve. • Feedback: whether team reactions affect later reviews. • Billing: what usage actually gets charged. My starting choice: file output, with a person deciding what to apply. Bug suggestions are only part of review; people still need to share code knowledge and agree on intended behavior. An empty findings list isn't merge approval. Source + setup: github.com/The-PR-Agent/pr-a… Plain-diff docs at that commit: github.com/The-PR-Agent/pr-a…

Give your AI video agent an editable motion library—with the selection rules attached. Remocn’s component index is a useful handoff for product-demo work: remocn.dev/llms-components.t… Three details worth carrying into the edit: 1. Meaningful numbers: choose animated-line-chart for time-series or animated-bar-chart for categories. infinite-bento-pan is for atmosphere: its counters and charts are decorative. 2. Timing: Length is motion at 30fps. glass-code-walk lists 150f = 5 seconds of animation, then you budget reading time. It depends on glass-code-block. Don’t treat 150f as total scene duration. 3. Duration types: sustained follows the scene; state-driven has no duration; transcript-driven must run through the final word’s endMs. The index also gives fit/rejection criteria, visual tone, dependencies and direct component docs. Let the agent shortlist there, then read only the selected pages. Adding a registry entry brings its registry dependencies along. The component source is copied into your project and stays editable. The repo documents an agent skill plus Claude Code/Codex plugins. Start with a Remotion project. Remocn is MIT; retain the notice. Remotion licensing and agent usage costs are separate: github.com/Remocn/remocn

Your Mac can run a team of AI specialists with different jobs, project knowledge and tool access. For recurring document work, that's the interesting part of webAI's new app. Elvis (@omarsar0) reports using smaller local models to expand his agent teams, and sees potential for AI R&D and custom harness optimization. The concrete resource behind that discussion is webAI's public Mac app. Here's my fit decision: shortlist it when you want specialists grounded in selected project files, with control over what each can access and do. These are the pieces to look at: Persona → a specialist's role. webAI documents choosing its knowledge and tools, and several Personas working on one task. Its launch example divides proposal work into extracting requirements, drafting and checking. That's a vendor illustration, not a reported customer result. webai.com/blog/webai-launche… Knowledge base → the files and notes supplying that specialist's context. The Persona page also separates Chats, Skills and tools, and Scheduled work. For a recurring project, this is the resource for understanding how its reference material and repeatable work are organized. webai.com/platform/personas Group → a shared project space containing people and Personas. Connectors → access to email, calendar and other services. Choose a Group for collaboration; choose connectors when the task actually needs information or actions in those services. webai.com/platform/webai-app The useful data-flow distinction: local model processing happens on your Mac. Web search, a cloud model connected with your own API key, connected accounts and sharing introduce other parties. Peer sharing sends selected content to recipients; routing infrastructure may help connect them. The app also collects account, device/usage, IP and diagnostic data. Local inference doesn't establish a zero-network-data guarantee. webai.com/webai-app-privacy-… Current entry conditions: Apple Silicon Mac, macOS Tahoe 26 or later, age 18+. Start at the actual download page: webai.com/download webAI advertises local inference without per-token charges. External model/service conditions still matter; that isn't a total-cost or all-features-free promise. My takeaway: the compelling choice here is a local team with deliberate roles, context and actions. For repeated file-based work on a supported Mac, that's a concrete resource to evaluate. Judge output quality on your own task before depending on the team.

16 fixes to one live iOS app. Sinan Yorulmaz reports that result for Cebbin; for SwiftUI/Compose builders, two useful details are native edit previews and a spacing scale configured for the app. His six areas: spacing, one accent, less decoration, copy, hierarchy and layout. Here’s how to use the resource for an app cleanup. 1. Set the app’s spacing scale before checking or rounding. For an app designed on an 8 pt/dp grid, this complete ai-design-check.json config sets the native base: {"spacing":{"native":{"base":8}}} Use your actual design system’s scale. Native code reads spacing from this config, with native taking precedence; otherwise it defaults to 4 pt/dp. A neighbouring Tailwind or Bootstrap theme does not define the app’s spacing. 2. Get file-and-line findings. From the project root, using the documented Cebbin folder: npx @enmera/[email protected] Cebbin/Views --json Replace Cebbin/Views with your app folder. The static checks include off-scale spacing, fixed text that ignores Dynamic Type and tap targets under 44 pt / 48 dp. 3. Preview the mechanical edits. npx @enmera/[email protected] Cebbin/Views --fix --dry-run This lists proposed changes without writing. On the default 4 pt/dp scale, upstream native tests cover SwiftUI .padding(13) → .padding(12), Compose padding(10.dp) → padding(12.dp), corner rounding and very heavy type → bold. These examples come from the upstream tests. Inspect the list before dropping --dry-run to apply it. Tap sizes, colours, emoji, words and hierarchy remain decisions. 4. Give the remaining design work to judgment. The repo’s ai-design-fix skill uses a four-line brief: job, claim, proof, keep. Install the documented skills with: npx skills add enmerasoftware/ai-design-check Then ask the agent to fix the screen using the report and the app’s existing design system. The six areas still matter after the numeric edits: • Spacing: do sibling gaps form one rhythm? • Accent + decoration: does the main action stand out, or compete with glow, shadows and sparkles? • Copy: do labels say what actions do, while preserving product meaning? • Hierarchy: which element leads? Changing heavy type to bold doesn’t answer that. • Layout: is the main action distinct from tabs, with room for long labels and empty/single-item states? Rerun the checker on the same files and inspect the actual app. A lower count is useful evidence about source patterns; it is not a complete visual, usability or accessibility verdict. Public MIT code, Node 20+. The checker and mechanical fixes need no agent or API key. Agent usage costs are separate. Repo and skills, at the reviewed commit: github.com/enmerasoftware/ai…

Start in a simple video UI. Keep the ComfyUI graph when you want more control. MOGFIELD, Trenchplanet's ComfyUI challenge studio, documents a useful handoff: configure a shot in the app, export its workflow, then change checkpoints, LoRAs or nodes in ComfyUI. The appeal goes beyond a text-to-video box. The docs cover character reference sheets, restyling a take, re-shooting from another angle, and Agent beta: film brief → storyboard → cast keyframes → shots, with approvals between stages. A project can download as one ComfyUI workflow. Two exports worth knowing: 1. Before generating: Video or Create Character → Open in ComfyUI downloads the API-format graph for your current settings. 2. After generating: History → Workflow downloads the graph that finished take actually ran. You get an editable starting point instead of reconstructing a setup from screenshots. Export guide: github.com/Trenchplanet/mogf… For a concrete graph to inspect, backlot_direct.json maps a take through depth → CameraRig → LensKit → GradeLUT → Letterbox → MP4. The repo labels it “ships”. It lists depth/recast/cast graphs as unrun drafts, and the depth/audio/mask preparation graphs as schema-checked without execution. Root workflow JSON is the server's API format; workflows/local contains editor graphs to open by hand. If adapting a server graph, preserve its BL: node titles: the server binds controls by title, not node ID. Graph catalog, status and title bindings: github.com/Trenchplanet/mogf… To use the app: Node 22+, ffmpeg/ffprobe, plus either a billed Comfy account/API key or your own ComfyUI with the required nodes and models. Partner nodes can still incur API charges in LOCAL. With no Comfy backend configured, the ffmpeg preview approximates camera/lens/grade; it is not AI video generation. The app code is MIT-licensed; node packs and model weights have separate licences. My reason to explore it: the simple interface leads to workflows you can inspect and adapt. The linked catalog tells you which starting points still need a first execution.

A product changelog can become a text-led Short with editable scenes, a review sheet and a cover. Here's the resource—and the exact handoff worth keeping. Remotion Shorts Skill gives a coding agent a ready kinetic-text project: topic → one JSON script → scene/cut review → 1080×1920 MP4. Choose it for animated tips or product updates; it doesn't edit existing footage. Keep these files together: • short.json: editable scene order, copy and theme, plus any owned local assets. • sheet.jpg: each scene; inspect line breaks, highlights and text inside the marked safe area. • cuts.jpg: opening frame and cut boundaries; look for awkward transitions. • report.json: scene timings and warnings. Read the copy at playback speed too. • short.mp4 + cover.jpg: watch and listen to the full export; check the cover separately. Those filenames come from the renderer. For short.json, its default destination is out/short/ inside the generated project. The format guide's changelog pattern is concrete: hook → 3–5 item scenes → end with one action. Number steps as strings “01”, “02” rather than ranking them; turn progress on. Include a stat only when you have a real sourced number. After the documented project setup, place short.json beside render.mjs and run from that project: node render.mjs short.json --stills This writes the sheets, cover and report, without an MP4. Fix warnings and inspect the sheets. Then: node render.mjs short.json Default export: 1080×1920, 30 fps, H.264 video/AAC audio. Keep the JSON and source assets with the MP4 so the next copy revision stays editable. Setup needs Node 20+, roughly 500 MB, dependencies and a headless-browser download. Background photos/video and music are optional local files; use assets you have rights to. Bundled fonts exclude CJK, Arabic, Hebrew and emoji. You can export locally without connecting a publishing service. The skill code is MIT. Remotion has a separate, eligibility-based licence; check its current terms for your organization and use. Repo + setup: github.com/social-media-skil… JSON format: github.com/social-media-skil… Remotion terms: remotion.pro/license

An ad-filled TV home screen became four streaming buttons and a dashboard for one household. The useful AI lesson is in how narrowly the owner scoped it. In his Nov. 2 thread, Ananto Mohammad (01Ananto) describes asking Grok Bot to build a custom LG TV screen without writing code himself. The starting brief: a dark screen for YouTube, Netflix, Prime Video and Jellyfin. He wanted to lose the ads, clutter and rows of apps he never used. His dashboard also included Apple TV and Xbox inputs, weather, front-door lock status, a backyard-camera bird count and football scores. The source image shows those elements together on one screen. The changes were small: turn the Xbox logo green, hide an HDMI input when nothing is connected, switch the glow color between day and night. He says every change got a preview before reaching the TV. That gives an AI dashboard build a concrete first milestone: the four-app navigation. Scope each device or live-data integration separately, then make visual changes one at a time. A screen layout and a working door-lock connection are different pieces of the job. The delivery detail matters too. He says Grok Bot built a small web app and installed it through LG's Developer Mode. He reports no new hardware or subscription for his setup. LG's official route is for installing, debugging and testing webOS TV apps. Its prerequisites include a networked webOS TV, a PC on the same network with webOS CLI, and an LG Developer account. Enabling Developer Mode reboots the TV. The session is time-limited. After it expires, a TV reboot disables Developer Mode and removes apps installed through it. You can extend the session while time remains. LG's requirements and session rules: webostv.developer.lge.com/de… The scope I'd borrow: a four-app dashboard prototype, with integrations priced and tested as separate work. A permanent household launcher needs device-specific deployment evidence, including what happens at startup and when someone presses Home. This thread doesn't establish that behavior for another TV.

A higher agent-eval score isn't enough to hand subjective work to a client. For AI-written copy or research reports, the useful question is whether the score tracks quality people actually value. Cameron Wolfe's written guide gives a three-stage route: 1. Define quality with people. Write criteria and scored examples; have reviewers grade the same outputs, then refine the guidelines where they disagree. Unclear criteria make a weak target for automation. 2. Scale checks offline. Calibrate LLM judges or task-specific rubrics against human-rated examples. Sample realistic, varied, challenging tasks. Use power analysis for sample size, confidence intervals for uncertainty, and repeated agent/judge runs for noise. 3. Validate in use. Compare a promising candidate with the existing system through A/B testing and production feedback before a broad rollout. Feed recurring failures back into the benchmark; keep refreshing its coverage and difficulty. My takeaway: a better score earns further evaluation, not automatic client delivery. Full guide, readable without signing in when checked: cameronrwolfe.substack.com/p…

Hand over your next AI-built Python project with a declared Python target, locked dependencies and exact check commands. The next person gets a setup they can reconstruct and inspect. Trail of Bits' modern-python skill is a useful resource for this. Here is the handoff recipe I'd take from it, with one update from current uv docs. 1. Pick the smallest useful container. One standalone file with external dependencies: use PEP 723 metadata inside that file. A multi-file app: use pyproject.toml. A new installable library: use uv init --package and its package structure. Keep an existing team's agreed tooling unless a migration is actually wanted. For an existing Python script named job_script with PEP 723 metadata (the filename has no extension): uv lock --script job_script uv run --locked --script job_script The explicit --script flag parses it as Python regardless of extension. Send job_script AND the adjacent job_script.lock. The skill's script reference still says scripts have no lockfile; current uv supports the adjacent lock above. Inline metadata also makes uv ignore the enclosing project's dependencies, so declare every external package the script needs. Script details: docs.astral.sh/uv/guides/scr… 2. For a uv-managed app targeting Python 3.12, make the tool target explicit. From the project root, add the development tools: uv add --group dev ruff ty pytest uv python pin 3.12 Merge these settings into pyproject.toml; retain its project metadata and dependencies, and merge existing tool tables rather than duplicating them: [tool.ruff] target-version = "py312" line-length = 100 [tool.ruff.lint] select = ["E4", "E7", "E9", "F", "I"] [tool.ty.environment] python-version = "3.12" This selects Ruff's default lint groups plus import ordering. It's a smaller starting point than the skill's ALL selection; Ruff notes ALL can gain new rules after upgrades. Match these targets to the project's actual minimum supported Python version; 3.12 is this recipe's chosen baseline. Ruff rule selection: docs.astral.sh/ruff/linter/ 3. Lock the handoff, then check it. After approved dependency/config changes, create or refresh the lock: uv lock The recipient runs these from the project root, stopping at any failure: uv sync --locked --group dev uv run --locked --group dev ruff check . uv run --locked --group dev ruff format --check . uv run --locked --group dev ty check . uv run --locked --group dev pytest Use --locked when the handoff must fail on a missing or outdated lock. --frozen skips the freshness check. Plain uv run can update the lock automatically. Lock semantics: docs.astral.sh/uv/concepts/p… Ship source, real tests, pyproject.toml, uv.lock, .python-version and the actual start command. Record uv's version and any system packages, private-index access or environment-variable names separately; keep secrets out. A 3.12 pin selects a minor line, not one exact patch. These checks cover lint, formatting, types and the tests you wrote. They don't establish that the utility does its intended job; include a real task-level example and result in the actual handoff. Requires uv installed and package/interpreter access; uv may download Python and dependencies. These are documented commands, not a reported project run. Adapted and expanded by DY from Modern Python by William Tan / Trail of Bits: github.com/trailofbits/skill… This adaptation: CC BY-SA 4.0. creativecommons.org/licenses…

A practical AI + Excel map for your next weekly report: 10 jobs, the inputs AI needs, and what a usable output should look like. Start with the data 1. Clean an export. Give Copilot the table and your rules for IDs, dates and duplicates. Its clean-data skill can propose corrections. Keep missing values visible; compare row counts and totals before accepting changes. This skill is still rolling out. Microsoft's skill guide: support.microsoft.com/en-us/… 2. Write formulas. Supply column names, the business rule and expected answers for a few rows. XLOOKUP defaults to an exact match and searches first-to-last: duplicate IDs can return the first record, not every match. Test a missing ID too. support.microsoft.com/en-us/… For multi-condition totals, use SUMIFS. Its sum range comes first; every criteria range must have matching dimensions. Check a filtered subtotal independently. support.microsoft.com/en-us/… 3. Diagnose errors. Give AI the exact formula, error and relevant input types. Ask for the cause and smallest correction. Check neighboring rows afterward; wrapping everything in IFERROR can hide the problem instead of fixing it. The formula references above are the starting point. Build the explanation 4. Find changes. Give Copilot the period, comparison period and metric definition. Ask for a PivotTable showing the change by category. Reconcile its totals to the source; a trend does not establish its cause. 5. Choose a chart. Give it the question: change over time, category comparison or relationship? Request the chart and its supporting values. Inspect units, date order and axis scale. 6. Build a dashboard. Give it the KPI definitions and intended filters. Request editable charts and PivotTables linked to the source data. Change one filter and trace a KPI back to its underlying rows. 7. Draft the executive summary. Give it the verified totals and comparisons. Request findings tied to cells or table values, with proposed actions labeled separately. Recalculate each quoted change; reject explanations the data cannot support. Copilot supports these analysis, chart, dashboard and summary building blocks. Chat mode keeps answers in the pane; edit mode changes the workbook. Choose deliberately. support.microsoft.com/en-us/… Look ahead 8. Build a forecast. Give AI the historical series, interval and assumptions to help prepare the model. Excel's Forecast Sheet on Windows provides an inspectable baseline with historical/predicted values and confidence intervals. Use consistently spaced dates; start the forecast within known history to compare predictions with actuals. Keep assumptions visible. support.microsoft.com/en-us/… Make it repeatable 9. Produce the recurring report. Give AI the approved reporting steps, period cutoff and required output. Separate workbook steps from delivery: Office Scripts handles workbook actions; Power Automate connects them to a wider flow. Check the reporting period and final artifact before enabling delivery. 10. Automate workbook chores. Give AI the named tables, desired changes and an explicit rule for reruns. Have it help draft an Office Script. Try it on a copy twice: the second run should not append duplicate records or overwrite unrelated sheets. A generated script is a draft until its behavior is checked. Microsoft's integration guide includes working tutorials for updating a spreadsheet, saving email content and sending weekly reminders: learn.microsoft.com/en-us/of… Access matters: these docs are public; the products are not universally free. Copilot needs an eligible Microsoft 365/Copilot plan, with features depending on platform and organization settings. XLOOKUP is unavailable in Excel 2016/2019. Office Scripts with Power Automate requires a business Microsoft 365 license; flow licensing also depends on the connectors used. Copilot eligibility: support.microsoft.com/en-us/… Save the map for the part of your next report you want AI to help finish.

Your AI-built site's hero can finish downloading and still appear late. Use this Chrome DevTools MCP setup + LCP decision map to find which part of the wait your coding agent should investigate. LCP measures when the largest visible image or text block renders. For an image, read its breakdown this way: • TTFB: navigation → first HTML byte. Check redirects, server response and HTML caching. • Resource load delay: first HTML byte → image request starts. Check late discovery, JS insertion and lazy loading; assess early HTML discovery, fetchpriority or preload. • Resource load duration: image download time. Check transfer size, responsive formats and delivery. • Element render delay: download complete → element appears. Inspect blocking CSS/JS and main-thread work. Compression targets download time. It doesn't resolve a separate rendering bottleneck. If LCP is text, don't assume there's a hero-image request to fix. Copyable setup For MCP clients accepting mcpServers JSON: { "mcpServers": { "chrome-devtools": { "command": "npx", "args": [ "-y", "chrome-devtools-mcp@latest", "--isolated", "--no-usage-statistics", "--no-performance-crux" ] } } } Requires a compatible MCP client, Node.js LTS, npm and current stable Chrome or newer. The repo links client-specific setup. The temporary isolated profile is cleaned up when Chrome closes. The other flags opt out of MCP usage statistics and sending trace URLs to CrUX. Your client/agent can still access browser content: use a suitable test session. Trace → decision 1. Get the actual target pageId from list_pages. Use navigate_page with that pageId, type "url" and your target URL. 2. Call performance_start_trace with that pageId, reload true and autoStop true. Navigate first; this trace reloads the selected page. 3. From the trace's Available insight sets, take an actual returned insightSetId. It is separate from pageId. Call performance_analyze_insight with the same pageId, that insightSetId and insightName "LCPBreakdown" when listed. 4. Identify the actual LCP element/resource and use the map above. Find its request with list_network_requests, then inspect it with get_network_request: same pageId plus its returned reqid. Drill into DocumentLatency, LCPDiscovery or RenderBlocking when offered. 5. Change the evidenced cause and rerun. Compare LCP/subparts under the same URL, viewport, network/CPU throttling and cache conditions. Keep both measurements; a lab trace doesn't establish real-user improvement. This MCP's lighthouse_audit excludes performance. Use performance_start_trace for this diagnosis. Apache-2.0 server source; model/client/runtime costs are separate. Official setup: github.com/ChromeDevTools/ch… Official LCP guide, pinned revision: github.com/ChromeDevTools/ch…

Your Excel tracker can become a mobile-friendly dashboard app—with AI handling data tasks inside it. In Glide Classic, the useful first choice is whether Excel is a starting snapshot or the workbook your team will keep updating. Snapshot / prototype → import Excel. Any plan supports import; Classic Free is for building drafts. Importing a file doesn't give you ongoing sync with later workbook edits. Keep working in Excel → use Excel Sync on Business or Enterprise. Store the workbook in OneDrive or SharePoint and authorize access through a Microsoft account. Choose this route when the original workbook needs to keep feeding the app. AI has a job beyond the dashboard: summarizing text or processing data in workflows. Business includes Glide AI. Both external sync and AI actions consume Updates; free draft-building isn't a free published AI app. For a durable synced app, settle the workbook's long-term home before connecting. Glide recommends SharePoint. Moving it between drives or SharePoint sites changes its ID and breaks references to the original tables. Preserve the Glide account that owns the connection. You can change the Microsoft account used to authenticate; that doesn't transfer the Glide connection owner. Excel's limit is 25,000 rows across all tables. The Business plan's larger high-scale data allowance doesn't raise that Excel limit. Choose import to explore the app. Choose paid sync when keeping Excel current is part of the requirement. These are Classic conditions; GlideOS has separate plans. Excel integration: glideapps.com/data-sources/e… Classic plans: glideapps.com/classic/pricin…

15,000 unread in Gmail. Start an AI-assisted audit with these 3 copy-ready searches. Craig Cherlet reported 15,800 inbox threads and 15,000 unread in an account dating to 2004. He connected Gmail to Claude, which proposed an inbox reset and sender audit. The cleanup was still a proposal in his post. Paste each query into Gmail's search box: 1. Old unread mail is:unread older_than:1y Review older unread mail for conversations that still need attention. 2. Older promotions category:promotions older_than:90d Review recurring promotional senders and decide which subscriptions to keep or unsubscribe from. 3. Older updates category:updates older_than:90d Review old notifications and decide what to archive while retaining records you need. For an AI-assisted sender audit, have AI help turn the sender patterns you choose to share into proposed rules, with a reason for each. Review those rules before applying them. These searches can include archived mail. Category results depend on Gmail's classification. Running a search filters results without changing your mail; the queries do not rank senders automatically. Google's operator reference: support.google.com/mail/answ…

Public web page → text your RAG pipeline can inspect, with one URL prefix. curl 'r.jina.ai/https://example.co…' That Jina Reader GET was run once without an API key on Nov 8 at 19:25 UTC. It returned HTTP 200 and 460 bytes of text/plain: Title: Example Domain URL Source: example.com/ Content (paraphrased): a note explaining the domain's role in documentation examples, including advice against relying on it for tests or monitoring. The useful catch: the same response warned that it came from cache and that page loading might be incomplete. HTTP 200 doesn't certify fresh or complete source content. For RAG, keep the source URL, retrieval time and those warnings alongside the extracted text. That lets you distinguish a successful retrieval from source material ready to support an answer. The documented controls address different problems: x-no-cache: true (or x-cache-tolerance: 0) requests origin content instead of a cached response. x-timeout waits for network idle or the deadline; x-wait-for-selector waits for a chosen page element. These controls weren't exercised in that single GET, and none guarantees a complete capture. Basic hosted reading currently allows 20 requests/minute without a key. An API key raises quotas and uses token billing based on content length. The hosted service receives the target URL; use public pages you may access, and keep private URLs, signed links and credentials out of this workflow. Source and header reference: github.com/jina-ai/reader

Want to understand what happens inside an AI model? Start with these 3 videos: neural networks visually → transformers → building micrograd in Python. They come from HackerTube, an AI-built catalog of 6,454 YouTube videos surfaced in Hacker News comments, across 150 topics. The useful extra: a downloadable public export you can curate yourself. Our starting sequence: 1) See the neural network 3Blue1Brown — “But what is a neural network? | Deep learning chapter 1” For the visual foundation: neurons, layers and handwritten-digit recognition. youtube.com/watch?v=aircAruv… 2) Connect it to LLMs 3Blue1Brown — “Transformers, the tech behind LLMs | Deep Learning Chapter 5” Move from a basic network to tokens, embeddings and transformer structure. youtube.com/watch?v=wjZofJX0… 3) Follow the mechanics in code Andrej Karpathy — “The spelled-out intro to neural networks and backpropagation: building micrograd” Build a tiny autograd engine in Python to work through backpropagation. Bring basic Python and some high-school calculus. youtube.com/watch?v=VMj-3S1t… How we narrowed it: we parsed all 6,454 records, kept machine-learning or LLM entries labeled evergreen with at least one recommender, then sorted by the source score. That left 61 matches. We chose these 3 introductory entries from the top 6 and reordered them visual → architecture → code; this is our learning sequence, not the catalog’s ranking. The catalog’s categories, summaries and recommendation labels are AI-generated and may contain errors. This shortlist comes from the data and checked video titles/authors, rather than a viewing review. YouTube playback availability can vary. Explore or download the catalog: hackertube.org/data Data credit: HackerTube + open-index/hacker-news, ODC-By 1.0. The data license does not grant reuse rights to the videos.

Turn a hardcoded web UI into a design system you can change in one place. Screenshot to Tokens provides an Argon Dashboard report + editable CSS tokens. In the creator's documented example, one shared text-color change improves 21 elements from 2.95:1 to 4.53:1 contrast. The finished report and generated token file are available to inspect before you run anything. It captures a live page's screenshot and computed styles. SAM 3.1 helps locate interface regions; Muse Spark proposes primitive → semantic → component tokens. A person can accept, rename, reject or merge them before the source rewrite. The Argon token chain, with its hex blue shown as RGB to preserve the exact color: --color-blue-500: rgb(94, 114, 228); --color-action-primary: var(--color-blue-500); --button-bg: var(--color-action-primary); That chain turns a color into a role, then a component setting. Change the action token and the button properties that alias it follow. Keep a separate text role when the same raw color serves a different job. The rewrite preserves each original value as a fallback. The practical payoff is a shared control point for later changes, with evidence of what the migration itself changed. The project's published point-and-say results make this concrete: a typed request to improve a muted label while keeping its gray changes the controlling token across 21 elements. Whole-page failing text pairs fall from 41 to 20, with zero regressions. These are the creator's recorded runs. The same record shows a requested red-button change blocked and reverted because “Month” would drop from 4.20:1 to 3.77:1 contrast. Shared tokens propagate mistakes as well as fixes; role selection matters. Start with these existing artifacts: Argon review report: s2t.sorbcloud.com/argon-run/… Generated CSS tokens: s2t.sorbcloud.com/argon-run/… The saved migration report is a separate baseline: fallback-only and merged-color checks both show 0 changed pixels. Loading all tokens changes 0.919% of pixels and is marked REVIEW for layout cleanup. Its PASS means the rewrite added no failing text-contrast pairs. It still reports 41 of 87 pairs below AA. This captured-route check excludes hover/focus states and non-text contrast; it does not certify an accessible app. My pick: use this when you need editable design roles in an existing app, and inspect the role map + migration report before adopting the tokens. The Argon file is a worked reference, not a universal theme for another site. Project + live demos: s2t.sorbcloud.com/ Source and detailed recorded results: github.com/nhunsaker/screens… The repo is Apache-2.0; retain its required notices. The offline demo uses stand-in providers. Real model modes need model access and may incur API/compute charges. Hosted demos share a limited budget and send page screenshots to hosted models: use public, non-sensitive pages only.

A browser agent can choose a valid button and still fail the task. For teams building browser workflows, browser-use/jev-ultrafast is a useful reference: it separates the next-action decision, execution, and checking whether the goal was actually achieved. Here’s the code worth reading—and the part I’d adopt. 1. Rebuild the menu from the current page. In the decision module, Jev gets the observed elements and chooses an operation plus a compatible target. Operation and target questions share one request. Only the target matching the selected operation is used. A separate text model supplies field values when the operation needs typing. Jev’s job here is choosing; it isn’t writing every field or inventing browser commands. Decision code: github.com/browser-use/jev-u… 2. Keep prediction separate from the action. The execution module exposes separate predict and act commands. An action must match the observed page fingerprint; the decision is consumed before execution. It records executed actions before observing the next page, so a failed follow-up observation doesn’t erase what happened. The inspector’s “Choose next” pauses before execution and shows the operation/target probabilities. That makes the handoff inspectable before switching to automatic runs. Execution code: github.com/browser-use/jev-u… 3. Treat DONE as a decision to verify. The README explicitly says a DONE choice still needs independent outcome verification. Its flight example searches and checks results; it doesn’t book a ticket. The MVP also excludes frames, uploads and pop-up tabs, among other browser features. Repository, inspector and limits: github.com/browser-use/jev-u… Where Jev fits: a decision with a defined set of answers. TypeSafe’s Choice returns the selected option, probabilities and confidence. Use a generative model for prose; keep exact limits in application code. docs.typesafe.ai/primitives/… One important distinction: confidence summarizes how concentrated the answer distribution is. It is not an accuracy percentage. TypeSafe recommends the selected option’s probability when routing depends on the chance of that option being correct; thresholds still need evaluation on your own task. docs.typesafe.ai/confidence For an existing worker router, I’d retain the original Jev + GrokBot setup’s shadow → logs → active progression: record proposed branches before letting them drive work, review disagreements, and keep a bypass. Keep irreversible actions under human control. Those are integration choices—not a claim that this repo includes a ready-made GrokBot connector or shadow-mode switch. Access and cost: the code is MIT-licensed. Live use needs TypeSafe API access; typing also needs a configured text-model provider. The current Jev 1.14/1.13 list price is $0.042 per million input tokens, with output tokens free. That is Jev inference, not a complete browser-workflow bill. docs.typesafe.ai/models docs.typesafe.ai/introductio… My choice: study this when you can define the next-action menu and verify the result separately. It’s a concrete reference for that handoff, rather than a reason to replace your entire agent stack.