Developer Launches KIRA, a Local-First AI Agent That Verifies Its Own Actions

·1 views

A developer has released KIRA Superapp, an open-source AI agent runtime designed exclusively for Apple-silicon Macs that prioritizes on-device processing and verifiable task completion. Unlike conventional AI assistants, KIRA only marks a task as done when its runtime receives confirmed output from the tool that executed it, rather than relying on the model's generated text. The system's decision layer, Orchestrator V1, runs locally via MLX and is built to plan tool use rather than produce conversational responses. Local storage covers chat history, memory, logs, and downloaded models by default, while voice features are handled on-device through Whisper and Kokoro ONNX. The project's code and installation instructions are publicly available on GitHub, and the developer is actively seeking technical feedback on real-world failures and edge cases.

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)

Markups: Free, Open-Source Browser Markdown Editor Requires No Account or Install

A developer named Nir Bhay has launched Markups, a free, open-source, browser-based Markdown editor available at markups.dev. The tool was built to eliminate the friction of account creation and cloud workspace setup that many existing Markdown editors require before users can begin writing. Markups offers a split-pane editing and live-preview interface, with support for diagrams via Mermaid, math equations via KaTeX, code blocks, linting, templates, and export to Markdown, HTML, or PDF formats. The editor is built on Microsoft's Monaco Editor — the same engine powering VS Code — giving it a familiar feel for developers. The project follows a local-first philosophy, keeping documents under the user's control without mandatory onboarding or cloud storage.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Neural Networks Explained: How AI Models Learn from Data

Neural networks are machine learning models made up of interconnected mathematical units called neurons, loosely inspired by how biological neurons in the human brain process and pass signals. A typical network consists of an input layer, one or more hidden layers, and an output layer that produces a final prediction such as a probability score. Each neuron calculates a weighted sum of its inputs, adds a bias value, and applies an activation function to determine its output. The weights and biases are not set manually but are learned automatically during a training process. Networks with many hidden layers are known as deep neural networks, which is the origin of the term deep learning.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Developer Rebuilds Failed Social App as CrowdWide to Boost Visibility for Small Creators

A developer has launched CrowdWide, a new social platform built from scratch after abandoning an earlier project called VibeConnect due to security gaps and incomplete features. CrowdWide is designed around communities, creators, and content discovery, with a focus on giving smaller or newer accounts a fairer chance at initial exposure. The platform experiments with a feed distribution model that allocates 40% of reach to new or smaller communities, 55% to established ones, and 5% to trending content. It targets a broad range of creators including developers, writers, artists, bloggers, and YouTubers, and already includes features such as scheduled publishing, nested replies, community moderation tools, and privacy controls. The developer acknowledges the algorithm is still evolving and expects it to change significantly once real usage data becomes available.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Go Developer Builds Ethereum Wallet CLI to Demystify Crypto Key Management

A software developer built a command-line Ethereum wallet in Go to understand the real mechanics behind cryptocurrency wallets, documenting key findings in a technical post on DEV Community. The project revealed that wallets are less about storing keys and more about a series of precise cryptographic decisions, where small implementation errors can break security or compatibility. Following the BIP39 standard, the wallet generates a 12-word mnemonic by combining 128 bits of random entropy with a 4-bit SHA-256 checksum, split into 11-bit groups mapped to a 2,048-word list. The mnemonic then seeds a hierarchical deterministic wallet tree via BIP32, allowing thousands of keys to be derived from a single master key, with hardened derivation requiring a parent private key rather than just a public key. The developer emphasized the project is intended for learning purposes only and not for production use.

0 comments Read more at DEV Community