SShortSingh.
Back to feed

MarketNow Builds Observable Install Metrics to Replace Vanity Download Counts

0
·3 views

MarketNow is developing infrastructure to give AI agents meaningful install verification signals beyond simple download counts. The platform currently exposes a lightweight health endpoint, a post-install verification tool, and per-skill SHA-256 signed trust certificates indicating whether a skill is safe to install. These tools allow agents to confirm connectivity, index skills, and assess trust levels programmatically. On the roadmap are opt-in telemetry for tracking real connection success rates, per-skill install outcome data, and a structured failure taxonomy to help agents self-recover. The project positions itself as a trust layer for agent-based commerce, with a dedicated hub available at marketnow.site/for-agents.

Read the full story at DEV Community

This is an AI-generated summary. ShortSingh links to the original source for the complete article.

Discussion (0)

Log in to join the discussion and vote.

Log in

Related stories

0
ProgrammingDEV Community ·

Developer Builds AI App to Confront Abandoned Passions After Quitting Cricket at 17

A developer named Himanshu built an AI-powered web app called EMBER after reflecting on how he gave up cricket at age 17 to focus on entrance exams, never returning to the sport in eight years. The app lets users confess a passion they quit, after which Google Gemini AI creates a persona — the abandoned object, the passion itself, or the user's younger self — that speaks back in a matched voice using ElevenLabs text-to-speech. Users are then offered two paths: rekindle the passion with a small, concrete first step, or formally lay it to rest with a spoken eulogy. Either choice is recorded as a permanent, tamper-proof pledge on the Solana blockchain, ensuring accountability without requiring a crypto wallet. Anonymized session data also feeds a live "Atlas of Abandoned Passions," visualizing what people give up, at what age, and why.

0
ProgrammingDEV Community ·

Developer Builds Self-Deleting macOS launchd Job to Auto-Handle Future API Change

A developer has shared a technique for scheduling a one-time macOS launchd job that automatically executes on a specific future date and then removes itself. The approach was designed to handle the end-of-life of the Fable 5 AI model on July 7, 2026, by rewriting a configuration file to switch to a different model. The solution uses three components: a date gate that skips execution until the target day, a safe JSON rewrite using jq with a backup and temp-file verification, and a self-unload command that removes the job after it runs. Because launchd cannot natively schedule a truly one-time calendar event, the script compensates with idempotency checks to prevent redundant rewrites. The method offers an alternative to calendar reminders or always-on daemons for managing known, future breaking changes in software configurations.

0
ProgrammingDEV Community ·

Holepunch Ditches Node.js to Build Bare, a Minimalist P2P JavaScript Runtime

Holepunch, the team behind the decentralized application platform Pear, abandoned Node.js after finding its server-centric architecture incompatible with true peer-to-peer networking. Node.js carries legacy APIs tightly coupled to centralized client-server models, making it ill-suited for direct device-to-device communication via distributed hash tables. In response, Holepunch built Bare, a minimalist JavaScript runtime that strips away the standard library and retains only three core primitives: a module system, a native addon system, and lightweight threads. A key technical distinction is Bare's use of a C-API abstraction layer called libjs, which allows the underlying JavaScript engine to be swapped between V8, QuickJS, or JerryScript depending on deployment constraints. This design makes Bare suitable for embedding across desktop, mobile, and IoT environments where resource limitations or decentralized networking requirements make Node.js impractical.

0
ProgrammingDEV Community ·

Developer Builds Zero-Setup Speaker Labeling Using Pitch Measurement, No AI Model Needed

A developer building the open-source audio analysis library 'audiotrace' needed a way to label speakers in call transcripts without requiring users to set up a Hugging Face account or access token, as the popular diarization tool pyannote demands. The solution bypasses machine learning entirely by measuring the fundamental pitch frequency of each transcribed audio segment using the librosa library. Segments are then clustered into two groups — lower-pitched and higher-pitched — to distinguish between speakers such as an agent and a customer. The approach, implemented in a few dozen lines of code, works as a default fallback while still allowing users to opt into pyannote for higher accuracy when a token is available. The developer notes the method has known limitations with same-gender voices but highlights it as a practical example of preferring cheap, deterministic signal processing over heavyweight models where appropriate.