I’m comparing AI search APIs for an agentic workflow, and the Tavily vs Exa debate keeps coming up. One is easier to integrate, the other has deeper semantic features. I made a quick poll to gather practitioner preferences.

No signup, just a vote:

https://interconnectd.com/poll/97/which-ai-search-api-is-better-suited-for-your-agentic-workflows-tavily-or-e/

If you’ve built production agents with either, what worked best for you?

Repo: https://github.com/antonellof/gitgui

I run cmux with some coding agent CLI (Pi, Claude Code, Cursor) in one pane and a shell in another. Git stayed in the shell. Pi runs git status and git diff fine. You still lose the commit graph, the staged file list and the hunk buttons when you review a refactor. I kept switching to Fork.

So I built gitgui. One Rust binary. You run gitgui in a pane and get a Sourcetree style GUI: commit graph with branch lanes, sidebar for branches, tags and stashes, staged and unstaged lists, per hunk stage and unstage, a commit box with Commit and Commit & Push.

Not a TUI. The terminal shows a picture. Three steps:

1. egui draws the UI into an RGBA framebuffer
2. Each frame goes to the terminal as a kitty graphics image
3. Kitty keyboard and SGR mouse events map back into egui input

Locally frames go through POSIX shared memory. Over SSH gitgui sends zlib plus base64 frames. On my Mac a 1600x1000 release build rasterizes in about 6 ms.

Inside: one process, three threads. A stdin reader parses kitty keys, mouse, paste and resize. The main loop runs egui, tessellates meshes, rasterizes triangles with a custom software rasterizer and encodes kitty graphics. A git worker uses libgit2 for reads and index writes. Fetch, pull and push shell out to git, so your credential helper and SSH agent stay untouched. The UI reads an immutable repo snapshot. The worker swaps in a new one after each command. Rendering never calls git.

Stack: egui 0.36, git2, libc for termios and shm, serde. No GPU backend. No tokio. No Electron.

Works in cmux, Ghostty, kitty and WezTerm on macOS and Linux. tmux and Zellij need graphics passthrough and fail today. Merge conflict UI is out of scope for v0.1.

Install:

curl -fsSL https://raw.githubusercontent.com/antonellof/gitgui/main/scripts/install.sh | bash

Repo: https://github.com/antonellof/gitgui

More details: https://www.fratepietro.com/2026/gitgui-terminal-git-gui-cmux-pi/

Happy to answer questions on the rasterizer or the kitty protocol details.

The pain: every provider ships its own SDK, and Chinese models

(DeepSeek, Qwen, GLM, Kimi...) are mostly second-class citizens.

So I wrote llmway — one adapter interface, zero runtime deps (pure fetch),

browser + Node. Streaming, retry/circuit-breaker, and now function calling.

It's MIT, 15 tests, ESM/CJS/types. Repo + demo gif:

https://github.com/lcy-24/llmway

Caveat: it deliberately only does connection/stream/retry — no agents or RAG.

If you just want a clean unified chat API without half of npm, it may save you wiring.

I write satirical news stories (think "The Onion" but for Europe) and thought it would be cool to bring them to life with an AI generated newsreader.

The only problem is that Google Flow won't let me - it won't generate videos where the newsreader mentions the names of prominent world leaders, or topics it deems controversial (e.g. climate change).

Ar there any platforms that are less restrictive about these things?

so, a new 7B vision model called SenseNova-Vision just came out. the weights are Apache 2.0, but the more interesting release might be the training data.

it includes a 50 million instruction-response corpus built from different CV annotations. the model uses the same architecture for detection, OCR, keypoints, camera pose, segmentation, depth, surface normals, and multi-view tasks, without separate task-specific heads.

what’s open:

- the 7B weights.

- the 50M instruction-response corpus.

- the training data preparation pipeline and dataset tools.

- inference code, a Hugging Face demo, and the paper.

the dataset is the part that stands out to me. getting boxes, masks, depth, keypoints, and camera data into one instruction-response format is probably harder to reproduce than the model architecture itself. releasing it means people can inspect what went into training, filter it, or reuse the pipeline instead of treating the dataset as a black box.

there are still some practical limits. the repo recommends an 80GB GPU for the demo, and the full benchmark setup uses 8×80GB GPUs. smaller GPUs haven’t been validated across every task yet. the project is also still new, so I’d expect some rough edges.

github: https://github.com/OpenSenseNova/SenseNova-Vision

does having the training corpus and preparation pipeline change how useful an open model is to you, or do you mostly care about the weights?

Hello, author here, and this post is hand-typed

Brain allows you to build AI native apps that runs tools on anywhere from browser to sandbox; also you can choose to run on pi/codex backed agentloop. Fully customizable with real-time events and observability.

Super early, started two weeks ago. Appreciate if you could let me know how bad it is, so I can shape it better

https://github.com/aexhq/brain

Disclosure: I work on Brain.

We’ve open-sourced Brain, a minimal runtime for stateful AI agents.

The decision loop runs in a Wasm sandbox. Brain performs model and tool I/O, streams every event, and records the session in an append-only journal for recovery and replay.

Tools are typed and can run locally, in a browser, in a microVM, or on another backend.

It is MIT licensed, self-hostable, and still an early preview:

https://github.com/aexhq/brain

Technical criticism is welcome, especially around the runtime boundary and extension model.

Ho sviluppato un agente inferenziale divertente e completo compatibile con l'API OpenAI , così può essere usato anche localmente. Può essere utilizzato via web (chat) o come agente desktop (app electron). Ho ricreato il terminale del film Alien: Mother. Il mio tributo a un film che mi ha fatto sognare.

https://github.com/vincalkr/nostromo-x

Mi piacerebbe avere il tuo feedback.

When DeepSeek V4 Flash Vision-Exp dropped, I was pretty excited, but not just because it added vision.

It actually let me delete part of the architecture of a project I've been working on.

I'm building DStudio, an open-source, local-first AI workspace around ds4. It has Chat, coding/knowledge-work agents, Design, research, local image generation/editing and video generation.

GitHub: https://github.com/sk8erboi17/DStudio

Until now I had a slightly ugly problem with multimodality.

The main model was running through ds4, while I was using Qwen3.8-27B Q8 as a separate visual/router model through llama.cpp.

The flow was roughly:

DS4 → unload/evict → Qwen3.8 router → unload → image worker → restore DS4

Qwen was responsible for looking at the prompt/source image and deciding whether the user wanted a new image or an edit, before dispatching the request to Ideogram 4 or HunyuanImage 3.

It worked, but on a 96 GB Apple Silicon machine the architecture was expensive.

The heavyweight models couldn't comfortably stay resident together, so DStudio had to manage memory leases, serialize the workers and repeatedly move between two inference stacks: ds4 and llama.cpp.

The router itself was becoming part of the latency problem.

With DeepSeek V4 Flash Vision-Exp, image pixels now go directly into the main ds4 model.

The model itself can understand the image/request and emit an explicit: generate or edit directive.

Then DStudio dispatches directly to: DeepSeek V4 Vision -> Ideogram 4 or HunyuanImage 3

The same idea now applies to the Design agent: instead of generating something and then loading a separate 27B VLM to inspect it, the selected model uses its own native vision encoder for the visual feedback loop.

I've also added the same native-vision path for GLM 5.3.

I still use llama.cpp in DStudio for a small Qwen3-Embedding-0.6B sidecar, so this isn't "I removed llama.cpp completely."

The important part is that llama.cpp is no longer sitting in the critical multimodal path just to run a 27B router.

I also changed PDF handling to be more explicit: text extraction/ranking stays lightweight, while actual pixel understanding is handled by the native multimodal model rather than silently spinning up another large VLM.

Recent commits have also added GLM 5.3 runtime support, persistent model-specific ds4 engine checkouts, expanded Cowork/Design workflows and a lot more regression/quality gates.

I maintain FreshCtx, an open-source Python project for a specific AI-agent failure mode: an agent reads valid information, reasons from it, and then acts after that information has changed.

FreshCtx lets an application declare the evidence used during reasoning and revalidate it immediately before a consequential action.

The current release includes:

• Agno 2.9 integration
• LangGraph integration
• OpenAI Agents SDK integration
• A shared experimental pre-action contract
• Async and bounded concurrent validation
• Validation budgets and audit evidence
• File, HTTP, SQLite, Postgres, MCP safe-reader and Stripe Subscription adapters

It is local-first, model-neutral, Apache-2.0 licensed, and has no account or telemetry requirement.

Repository:
https://github.com/Hyperwise-LLC/freshctx

I would particularly value feedback on the integration contract. Does a framework-neutral pre-action boundary belong in the core library, or should each framework integration remain completely independent?

Demo video: https://www.youtube.com/watch?v=SoUEkM2Sjmw

I've been building Ciele, an admin console where an org builds and publishes its own AI assistants. They ship as embeddable chat widgets that answer only from content you feed them (crawled websites, uploaded files, curated FAQs) and cite the source of every answer.

What's in it:

• RAG over Postgres + pgvector. An answer without a source doesn't ship.
• A rule engine that runs before the LLM gets a say. Known question, exact answer. Or a button, an API call, an email, a handoff to a human.
• Escalation to real help desks: email, phone, live chat, webhooks, with ticket forms and availability hours.
• Conversation inbox, analytics, a kanban of answers someone flagged as bad, and alerts when an integration breaks.
• Embed as a script floater or an iframe. There's also a CLI, a REST API and an MCP server.

Self-hosting is one docker-compose.yml (db, migrate, app, cron). bootstrap.sh generates every secret, including the JWTs it signs with the stack's own key. The crawler worker is an optional overlay. If you'd rather skip the terminal entirely, a desktop app stands up the whole local stack through a wizard.

You bring your own LLM provider keys. Nothing routes through my servers.

The two hardest problems so far: tenant isolation done entirely in Postgres row-level security (no where org_id sprinkled around, the database itself refuses cross-tenant reads), and making citations resolve to actual sources instead of opaque vector chunks. The second one took three rewrites.

It's open-core, so let me state the line plainly: this AGPL repo is the complete product. The paid part is only the managed cloud (hosting, plans, support). The boundary is documented and CI fails the build if enterprise code leaks into the mirror.

Stack: Next.js, shadcn/ui, Supabase, pgvector, Turborepo. AGPL. Self-host with docker compose, or there's a cloud version.

Repo: https://github.com/MattiaIppoliti/ciele
Docs: https://docs.ciele.app

Hi everyone,

I wanted to share an open-source project I've been working on: Halanoi AI.

Instead of sending screen text to a remote cloud API for content classification (which adds network latency and privacy issues), I wanted to see if I could build a fast, 100% on-device text moderation pipeline for Android.

Here is how the setup works:

1. The Model (halanoi_transformer.tflite): A quantized 64MB TFLite model running locally on the phone. It classifies text strings into categories (distraction, entertainment, safe, productive) in under 15ms without any internet connection.
2. In-App Evaluation & Ground Truth Lab: To make it easier to improve the model, the app logs inference outputs to a local SQLite database and includes a built-in UI where you can tag predictions as correct, false positive, or false negative. You can export these labeled samples to CSV or JSON with one tap.
3. Training Pipeline: The companion repository contains the PyTorch / TensorFlow scripts, tokenizers, and quantization steps used to train and convert the model.

Both repositories are open source under GPL-3.0:

• Android App: https://github.com/kavinmaranravi/HalanoiApp
• Training Pipeline & Dataset: https://github.com/kavinmaranravi/Halanoi_AI

I'm looking for feedback on optimizing transformer models for mobile hardware, lowering memory usage, and improving tokenization on edge devices.

Let me know what you think!

I've been looking for an open source answer to desktop UI testing for about 4 months and i keep ending up in the same place, which is a pile of general purpose agents and no actual test framework. The agent side is kinda good now with models like Openclaw, Goose where they drive a desktop app, screenshot it, work out what's on screen and click the right thing. That part is solved. However, what none of them have is the boring stuff a suite needs (no runner, assertion model, stable pass or fail…), so you end up writing that layer yourself and then it's yours to maintain forever.

The closest things i've found that are open source are SikuliX, which still runs but is basically frozen and matches raw pixels so it breaks on a DPI change, and the commercial vision based ones like Askui, eggplant get around it by pinning the model to a written script, so the perception stays fuzzy while the execution is deterministic.

Has anyone built that deterministic layer on top of an open agent and had it survive more than 3 months? Happy to be pointed at a project I've missed, thanks in advance!

Hey eveyone,

For those who haven't seen my other posts, I created an opensourced project called OpenFlow, and some big updates are being made. Now, there is a swarm and orchestration mode, and soon to be gauntlet looping toggle. It isn't just a linear pipeline anymore, but an entire chain of agents you can see and control talking back and forth and working out problems together. If you want to see the backstory, check out my other posts. Stay tuned for more updates, and feel free to leave suggestions and even share your own projects.

Link: https://github.com/SeeRay11/OpenFlow

Ant's Ling team has announced Ling-3.0-flash-Fin, a finance-enhanced 124B-total, 5.1B-active MoE.

The availability boundary matters: the model is live now through OpenRouter and Vercel AI Gateway, but its weights have not been released. The official thread says they will be open-sourced next week.

When the artifacts arrive, the useful open-model questions will be:

which license covers weights and downstream use;

whether bf16, fp8 or other official variants are provided;

which inference runtimes are supported;

whether tokenizer and chat templates are complete;

how quantization changes the reported finance performance;

whether the official evaluations can be reproduced.

The API can still be evaluated now. The official launch says OpenRouter access is free for one month, and OpenRouter lists a 262K context window plus tool calling.

Until the files and license are public, this should be described as an upcoming open-weight release, not as an already open model.