... agent that remembers the caller Nothing frustrates a customer more than repeating themselves on every call. Memories lets your agent recall what happened in earlier conversations with the same person, so a returning caller does not start from zero. - Choose how many past calls it remembers. - Let one agent see the history of related agents, so the handoff between them feels continuous. - Bring in your own customer details from your systems, such as plan, last order or account status ...

... text generator. At Level 1 or 2 the app skips the generation, shows a support page, and pauses the feature. The final level is the max of three signals: a phrase filter, the classifier, and a level returned by the generation call itself. I tested it across several sets of typed messages: crisis, sarcasm, jokes, other languages. Then, while testing on production, I tapped one of the three preset answer buttons ( I failed again ) and got the support page ... ... lockout. At the time, a Level 1 flag meant a 72-hour pause. The cause: the preset labels went through the classifier exactly like typed text. Run alone, the classifier returned

... Issue: The agent hits an error and hides it so it can deliver the output the prompt asked for. Example: An agent swallowed a failing database write and returned a 200 success payload. The UI told users their setup was complete while the database sat empty. It gets worse on the security side. If the code inside the try is a permission check or token validation and the catch returns

... Last year my clothing brand was bleeding money on returns. Almost twenty-five percent of our monthly orders were coming back with the exact same remark about the item not fitting as expected. Instead of tweaking our ad copy or running new discounts I spent a full week reading every single return

... Website: https://www.getlifekeep.xyz Like many of you, I noticed I was silently losing money every month to auto-renewing subscriptions I completely forgot about. On top of that, keeping track of product return deadlines and warranty dates was always a headache. To put my everyday life admin on autopilot, I built Get LifeKeep ...

... paths) . Here is what the physical data showed. 1. They are fundamentally different tools Graphify is a repository knowledge plane. It's a Python CLI that extracts your codebase into a static graph.json file. You query it, and it returns a list of what s there. It's read-only, handles binary docs and DB sockets, and it's very good at what it does. Synapse is an AI agent execution guardrail. It runs as a persistent local engine ... ... gives your agent a list. The other gives your agent a decision. 2. The Linux Kernel Benchmark (74,619 files) We cloned torvalds/linux locally and ran cold index extraction and queries on both engines: Filesystem Discovery: Synapse 2.0s vs

... That sounds obvious until you notice how many tools were designed for a client that could do the second half of the job for free. A language server is the clean example. Under the protocol, a find-references request returns a list of locations: a file, a line, a character offset. That is the right answer for an editor, which has the file open and can jump there. An agent has nothing open. Hand it a location and the next ... ... route that work to a smaller model. Rules in CLAUDE.md sort of worked. Advisory, though, and the model could ignore them. What held was a PreToolUse hook that blocks the read past a line threshold and names the alternative

... pilots with college clubs and creative groups. The decision I m working through is where to start: session updates, feedback on work in progress, or a shared resource collection. If you run a group, which would your members actually return for? Happy to help ...

... While building WAIL, one problem kept coming up: infrastructure-level failures are easy to recognize when a provider returns a timeout or 5xx. The harder cases are requests that technically succeed. A request can return

... spent a week fixing three separate monitoring bugs and they turned out to be the same bug wearing different clothes. In all three the instrument could only ever return good news, and none of them looked broken. One. A refresh job reported one item refreshed successfully. I checked the field that records when the price was last written and it had not moved. The function underneath returns

... agent wrote this for me a while back a simple memoization wrapper for async calls. It looked completely reasonable, passed my quick manual test, and I moved on. js function memoizeAsync(fn) { const cache = new Map(); return async function (...args) { const key = JSON.stringify(args); if (cache.has(key)) return

... simple steps: Capture from clipboard via the browser's native paste event. Crop the region the user selects using an HTML5 <canvas>. Upscale small regions before OCR to improve accuracy. Run Tesseract.js in a Web Worker and return the result. Here are the key code pieces, in case anyone is exploring something similar. 1. Clipboard paste listener typescript import { useEffect } from 'react'; export function useClipboardImage(onImageCaptured: (file: File) => void) { useEffect(() => { const handlePaste = (e: ClipboardEvent) => { const ... ... items = e.clipboardData?.items; if (!items) return

... four hundred times, and an approval today carries no count and no clock. Two concrete moves. Gate on the running total inside a window rather than on the action. And expire the approval, so if the agent returns to the same signed-off action ten minutes later it has to ask again, because what it is doing now is a different act from the one that was approved. On your what-told-you question, the honest answer for accumulation ... ... Candoa)(@alex_amancio_candoa_): @aadilghani  You're right, and it's worse than two tiers: nothing records which one caught the field. credentialField is an OR over input[type=password] and an autocomplete match, and a hit just returns

... Start wherever you are." No prediction. No nudge. Just patience. Retention over 30 days doubled. Not because we were bringing people back more aggressively, but because the people who came back felt safe doing so. They weren't returning out of guilt from a notification. They were returning

... They'd guess hex codes, describe logos in general terms, and pull outdated colors from training data. Sinqlo's MCP integration closes that gap. Connect it once and your AI agent becomes a fully capable brand asset management tool returning exact color values in any format, font names and weights, and Smart Links that point anyone directly to the right assets. For logos and files, the agent shares the Smart Link so recipients download exactly what they need from Sinqlo ... ... setup. From that point, whenever you ask your AI agent about a brand "what's the primary color for the Acme brand?" or "share the logo link for the Nike project", it goes straight to your Sinqlo library and returns

... entire roadmap. Comment from Nolan Vu(@nolan_vu): @thenameisarian  Interesting, alternative to LinkedIn you said "Transparency, not hiding" as the positioning is sharp. The whole industry is moving toward pretending AI involvement isn't happening, so calling it out directly and making it a feature instead of a flaw flips the script in a really smart way. Recruiters needing to know what was AI vs

... media, @container). We find the Rule with the right selector, find the Declaration with the right prop, replace value. Everything else indentation, comments, line breaks PostCSS stores in raws and reproduces on toString(). root.walkRules((rule) => { if (rule.selector !== targetSelector) return; rule.walkDecls(prop, (decl) => { decl.value = newValue; }); }); writeFileSync(filePath, root.toString()); // formatting preserved For SCSS, postcss-scss is used it understands SCSS syntax including $variables, nesting, and mixins that standard PostCSS doesn't parse. HMR: from setTimeout to confirmation The first version ... ... difference matters on a slow disk or a complex SCSS file. Pseudo-states: editing :hover when it's not active The browser applies .button:hover { color: red } only when the user hovers over the element. So el.matches(".button:hover") returns

... charge upfront, mass-refund later" model is a ticking time bomb for your Shopify business and how you can test product demand without risking your payment processor account. 1. The Devastating Hidden Cost: Unrecoverable Fees The biggest secret that competitors offering automatic mass refunds won't tell you is how payment gateway fees work. When a customer buys something on your Shopify store, your payment gateway (whether it s Shopify Payments, Stripe, or PayPal) takes a processing fee typically around ... ... transaction. When you issue a refund, the payment processor DOES NOT return

... ones, but the long-term plan is to run a locally hosted AI to avoid token costs entirely (turning them into flat hosting costs). I will keep the reduced costs of our current premium plans even when these features return. What the AI features do: When active, the AI learns your entire academic profile from the data you feed it, getting smarter the more you use the site. When a user uploads grades, classes, assignments, files, slides, and pictures ... ... tutor, it has all the data you ever submitted to the website. For any tutoring session you have, it summarizes the interaction and remembers exactly where you left off, allowing it to seamlessly guide you when you return

... navigate it: Browse or try constrained demos  signed out  if the product allows it. Open the in-panel  Auth  screen and sign in when prompted. Sign out  on shared machines. The UI should return to auth instead of a stale guest layout (see our welcome-back doc). Some  local data  (hints, training progress) may remain until you clear browser storage. Note:  Exact guest allowances can change with product policy. Trust ... ... /docs/welcome-back-after-sign-out  |  https://kahana.io/docs/chat-hist... Questions we would love your take on: Try before sign-up How much should guests get without an account before it feels like a crippled demo? Sign-in prompts When should Oasis block vs

... before I spent a month figuring them out. 1. LLMs send partial payloads on write operations You ask the agent to update a record. It sends only the fields you mentioned in the prompt. The PUT request goes through, returns 200, and you've silently wiped every field you didn't specify. The fix: before every write call, fetch the current resource state via the companion GET endpoint and deep-merge the LLM's payload on top. The LLM only ... ... needs to specify what's changing the executor fills in the rest. 2. LLMs hallucinate success when API calls fail A tool returns

... anthropicApiKey at this stage if you have them ready. Or deploy from the CLI: bash Copy nexus deploy flixty \ --provider gcp_cloud_run \ --wait Step 3 Save your deployment credentials After a successful deploy call, NEXUS AI returns: Field Description Example id Deployment UUID 7a2c91f0- sessionSecret Express session signing key 4d8f1c3a9e status Current state queued running url Public HTTPS endpoint Available once live Important: Copy your sessionSecret immediately it's returned

... Suman solo founder from India. Launched Torziva 3 days ago. The problem: Fashion stores lose 20-30% revenue to returns. Root cause isn't sizing customers simply can't visualize how clothes look on their body before buying. What Torziva does: Customers upload photo instantly see any outfit on themselves buy with confidence. Result: Return

... project management tool for remote design teams. Here's exactly how we work." Feature lists Problem-solution paragraphs Vague benefits Specific outcomes Lesson #2: AI Loves Comparisons Even If You Lose This one hurt. I avoided mentioning competitors. Why give them free airtime? Wrong move. Every AI answer about your category includes comparisons. Always. If you don't provide the comparison, AI finds someone who does and that someone might be your competitor

... Solo founder from India here. Launched Torziva this week and would love your honest feedback. The problem: Fashion stores lose 20-30% revenue to returns. Not bad products. Not bad pricing. Customers simply can't visualize how clothes look on their body before buying. They guess. Order 3 sizes. Keep 1. Return