QBitFlow Puts Referral Payouts On-Chain, Promising Automatic 20% Fee Split

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Crypto payments platform QBitFlow has launched an on-chain referral program that automatically pays referrers 20% of the company's transaction fee the moment a referred merchant's payment settles on the blockchain. Unlike traditional affiliate programs that rely on company dashboards and manual payout schedules, the split is enforced directly by a smart contract with no claims or invoices required. The reward comes out of QBitFlow's own 1.5% flat fee, meaning referred merchants pay nothing extra. Any QBitFlow or qbf.cash user can participate and earn the referral cut for six months per referred user. The contracts are open-source and publicly auditable on GitHub, and the program uses Permit2 to standardize ERC-20 token approval into a single signing flow.

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Claude Sonnet 5 matches Opus performance at lower cost, now available via API

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ProgrammingDEV Community ·

Developer builds TypeScript compiler graph MCP tool, cuts Claude Code token use tenfold

A developer has released @ttsc/graph, an open-source Model Context Protocol tool that gives AI coding agents a pre-built index of a TypeScript codebase derived directly from the TypeScript compiler. Instead of returning raw source code, the tool provides function names, call edges, type signatures, and exact file-line coordinates, allowing agents to answer structural questions without crawling through files. Benchmarks show the tool achieves roughly 10 times fewer tokens on open-ended codebase questions compared to a grep-based baseline, with answer quality remaining comparable. Existing tools such as codegraph, codebase-memory-mcp, and serena were evaluated but found inconsistent across repositories of varying sizes. The project is publicly available on GitHub, with full benchmark methodology and per-repo results published on the project's documentation site.

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ProgrammingDEV Community ·

Guide Shows How to Sync AI Agent Memory Files With Obsidian for Easy Editing

A developer tutorial published on DEV Community explains how to make the long-term memory files of Hermes Agent, an open-source AI agent released in February 2026 by Nous Research, editable through Obsidian, a markdown-based note-taking app. Hermes Agent stores its memory across sessions in plain-text files using a § character as a delimiter, a format that is difficult to edit manually and offers no version history. The proposed solution uses a single ~400-line Python script, relying only on the standard library, to sync these memory files into cleanly formatted Obsidian markdown notes with YAML frontmatter. Python was chosen over bash because the multi-byte § delimiter can be mishandled by bash string operations, whereas Python's re.split() processes it reliably. The system also incorporates automated Git commits every six hours, giving users a full change history and a safety net against accidental data loss.

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ProgrammingDEV Community ·

Why AI Teams Need a Metrics Baseline Before Scaling Any Feature

Software teams building AI features often struggle to evaluate whether those features are actually working once usage scales up. A metrics baseline provides a small set of before-and-after measurements to determine if an AI workflow is improving, degrading, or simply becoming more costly. Unlike generic software tracking, AI features require additional signals because model outputs are probabilistic and can be fluent yet wrong, correct but incomplete, or useful but prohibitively expensive. Key baseline categories include cost per successful task, output quality, latency, user adoption, and real-world task improvement. Experts recommend starting with just one or two metrics per category tailored to the feature's specific risk and purpose, rather than building sprawling dashboards that obscure decision-making.

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