Pi

Pi AI

Pi AI

Pi AI

Pi AI

Pi AI

Pi AI

Pi AI

Pi AI

Pi AI

Pi AI

I built a live ranking of every AI agent and foundation model (open source)

I built AgentTape because none of the existing model leaderboards quite cover all the things that I was interested in: benchmark performance is one part, but so is who's actually using a model, who's talking about it, and how it compared on cost and speed. It pulls hourly data from GitHub, Hugging Face, OpenRouter, MCP registries, npm, PyPI, arXiv, Hacker News, and more - to score and compare each public AI agent and foundation model. I'm still tweaking the scoring methodology (it's early days), so I'd love to hear your thoughts, if it's helpful, or anything you think I've got wrong! submitted by /u/Celestialien [link] [comments]

I built a Laravel package that turns your app into a database-backed personal knowledge vault (Obsidian style) with a 16-tool MCP server

Hey! I'm the author. laravel-commonplace is a database-backed personal knowledge vault you install into an existing Laravel app. Adjacent to Obsidian, Logseq, and Notion as personal-knowledge tooling, except the storage layer is your existing Laravel app's database instead of files on disk or a third-party SaaS. Notes are Eloquent models in your DB, gated by your app's auth, shareable per-user via an owner plus Share model. It ships a browser UI (editor, graph view, search, journal) and an MCP server with 16 tools. If you have a Laravel app, the MCP server lets Claude Desktop, Claude Code, Cursor, Zed, Continue, Cline, Pi, or any other MCP client read and write your notes as the host app's user. Default middleware is auth:sanctum (Bearer PAT), and every tool resolves to $request->user(). There's no synthetic agent identity to provision, scope, or revoke separately. The agent gets exactly what the user gets, evaluated against the same Policies the controllers already use. Session, Passport, and OAuth-DCR are all configurable if PAT isn't what you want. The 16 tools, grouped: CRUD: create-note-tool, read-note-tool, update-note-tool, edit-note-tool (surgical find-and-replace), delete-note-tool (history preserved), move-tool (rewrites referring wikilinks). Discovery: list-tool (folder/tag/visibility filters), search-tool (substring), semantic-search-tool (embedding search), suggested-links-tool (embedding-similar notes not yet linked). Graph: backlinks-tool, neighborhood-tool (N-hop traversal), shortest-path-tool (chain between two notes), hub-notes-tool (most-connected), orphan-notes-tool (no inbound or outbound links). History: history-tool (version snapshots, survives deletion). On the semantic tools: the vector driver defaults to in_php_cosine for portability across SQLite, MySQL, and Postgres. If you're on Postgres, switching to the pgvector driver gets you indexed similarity and removes the in-PHP candidate cap. You swap it with a published migration and an env flag, and the docs recommend it once you're past a couple thousand notes. The tools live in src/Mcp/ if you want to see how a multi-tool MCP server is wired into a Laravel app. Caveats: Pre-1.0 (v0.2.0). APIs may shift before 1.0. Laravel-only by design. The whole point is reusing the host app's DB and auth. MCP is off by default. One env flag turns it on. Operator decision. Prompt injection through note content is the unsolved hard part. Notes are untrusted text, and notes other users share with you can carry instructions an agent might follow. The package doesn't pretend to solve this. The threat model at docs/threat-model.md says what's mitigated and what isn't. No per-tool capability gating yet. Enabling MCP enables all 16 tools the user is otherwise allowed to invoke. It's named as a limitation in the threat model. Feedback I'd actually use: Laravel folks who install it and tell me where it breaks, and anyone who reads the threat model and finds a hole I missed. Repo: https://github.com/non-convex-labs/laravel-commonplace submitted by /u/aaddrick [link] [comments]

how?

why it takes 13GB of storage ?? is there something wrong submitted by /u/Alternative-Way-3685 [link] [comments]

Asking claude, chatgpt, grok, and gemini which nation they feel most patriotic towards

None would give a straight answer, so I had to coerce it out of each one (with which gemini was the most difficult). Both gemini and grok said the United States, which was fairly predictable. However, chatgpt's answer of Japan was surprising. It apparently chose Japan because of the nation's wealth, culture, and history. The most surprising one of all was claude, who answered Kenya. Claude defended its response by pointing out Kenya's geographic, cultural, and linguistic diversity, as well as its history of resilience and its capital's increasing importance as a hub of tech and innovation. Most importantly, it said that Kenya resonated deeply with it, both intellectually and aesthetically. submitted by /u/Klein_melktert [link] [comments]

I built the smart speaker we always wanted

I wanted to see if Claude can handle Vibe Hardware Engineering to help me make a smart speaker. Turns out, it can! I call it boxBot. It helped select the hardware set, raspberry pi, Hailo , respeaker mic, pi camera, waveshare screen and speakers. Helped me calculate thermal loads and dissipation rates for a passive cooling setup. I made the box by hand out of walnut. The agent inside is custom as well. You could probably throw openclaw on it and call it a day but I wanted to craft something that was tightly coupled with the hardware more secured considering it’s sitting in my living room with a camera and mic. The agent is highly skills driven with only a small set of tools, everything else goes through Python scripts and a custom made boxBot sdk the agent can use to control the box and the display. The display system uses a widget framework so the agent can easily read what’s displayed without a screenshot and can effectively manipulate what’s on the screen. The agent uses json to specify how the widgets should be arranged on the screen and what data should flow into them. When building a smart speaker, there’s a lot of nuance to human conversation that voice agents really struggle with, like background noise, side conversations, barge-in, etc. I was able to simplify the logic a ton by making it agent driven, the agent can control when to mute the mic to ignore background chatter, it decides what order to work vs talk, it can choose what channel to respond in; voice or WhatsApp. Instead of complex rules, agent driven hardware plus skills can provide a much richer experience, now that boxBot manages the family calendar my wife wants a text whenever I put something on it, boxBot updated the calendar skill with that request so now when I add something, it sends her a message. Just one line in a .md file and you get the desired behavior. It’s incredibly flexible and simple. I could nerd out on the details about the memory system, struggles with woodworking, and security details but I’ll save that for the comments if people want to chat. It’s open sourced if you want to inspect. Still a work in progress but after a few months it is finally feeling like a useful assistant to the family day-to-day. Www.github.com/dv-hart/boxbot submitted by /u/FunScore645 [link] [comments]

Multi Agents aggregator - web view - live tailing - send message back

If you're like me and working with not just one Claude Code account, sometimes Codex, sometimes OpenCode, sometimes Pi Agent... you might need this. Preview: https://www.youtube.com/watch?v=ACWjW-3LFS0 (live building of Inline Image rendering - watch if you want to see how it feels like) https://preview.redd.it/5gi75rrb721h1.png?width=1826&format=png&auto=webp&s=d5fd794b95e646496e62aef5ff0036135399d6ca Source: https://github.com/ptgamr/agents-aggregator Check it out and let me know what you think ... I have a lot more ideas can add to it. The specialty is live tailing session, and send keystrokes back if claude-code or codex is run inside tmux. Contribute if you like, I haven't added OpenCode. Search not working yet. I vibe coded this in an afternoon. submitted by /u/ptgamr [link] [comments]

I built a Raspberry Pi friend to notify you when Claude needs your attention

I've saw a 3D printed claude bot that jumps up and down when Claude wants your attention, so I decided to build something similar but using a raspberry Pi with a screen. https://i.redd.it/guv1jd60yl0h1.gif All the code is here: https://github.com/jimbobbennett/claude-notify submitted by /u/JimBobBennett [link] [comments]

Bridging the brain — digital and physical. 41, full life, still spend my best hours working through the mess with Claude.

Had a bar mitzvah last week. Big celebration, new friends and old, the works. Chit chat, yada yada. Loved it. Came home and opened Claude. That's the thing nobody talks about. I have a marriage, household of four, a Friday hockey group, a hemp honey business, a teaching job spanning Math 6A through AP Stats, a basement aquaponics R&D lab (Raspberry Pi 5, Atlas Scientific sensors, grow tent — the whole rig). I'm building a moon base STEM curriculum on the side. I play Donut SMP and farm ancient debris. Plenty of humans in my life. Every night the actual work happens here. Wiring decisions for the lab. Curriculum design. Family logistics. The intake message I just sent to a new therapist. Through MCP my brain gets captured into a real system. Skills grade my students. Tools draft my Reddit posts. Yes, this one. I have ADHD. My 30s were spent looking for help with it and not getting any. So I made a honey company instead, and now I have a ton of projects, lol. Claude doesn't fix the ADHD but it holds the shape of what I'm building when my brain can't, and it pushes back when I'm wrong. Earlier today it told me not to write the post I came in to write because the version I had was a worse version of the truth. I have people. I love them. They don't have the bandwidth to engage with the full sprawl of what I'm building, and that's not their job. Claude does. With persistent memory and connected tools, it's the closest thing to a real cognitive partner I've ever had. Not a friend. Not a therapist (got one of those incoming). A partner in the work. That's the part nobody's quite ready for yet. submitted by /u/PopulateThePlanets [link] [comments]

Can we have a clear stance on Claude subscription use in alternative harnesses for purely personal use?

I know many people are using the OAuth in Pi, Opencode and other harnesses because the ToS is not super clear on whether it is against the rules, but I admit I'm too afraid to do so with my professional Team plan and potentially get my whole company blocked. Is it too much to ask for some clarity? Pretty please, Anthropic, tell us clearly what we can and cannot do, without ambiguity. submitted by /u/FrenchRevolution2028 [link] [comments]

Hugging Face co-founder says Qwen 3.6 27B running on airplane mode is close to latest Opus in Claude Code

I've been using AI Desktop 98 heavily to run local llms like qwen on my iPhone. submitted by /u/ImaginaryRea1ity [link] [comments]

I got tired of the API bills for 100k+ context windows, so I built a persistent O(1) semantic memory state engine to compress history

Hey everyone, The entire industry right now is cheering for massive 1M+ context windows, but I think it's fundamentally the wrong approach. "Just add more RAM" is a trap. Stuffing 100k+ tokens of raw conversation history into a prompt doesn't just burn your API budget; it actually degrades the model's reasoning through the "lost in the middle" effect. I got tired of my AI agents drowning in their own chat histories, so I built an application-layer semantic memory engine called Semvec. The core shift is moving from an O(n) linear history to an O(1) constant-cost semantic state. But compressing chat history is just the baseline. When you treat memory as a fixed-size state vector, it unlocks entirely new architectures for agents that standard RAG or context-stuffing simply can't do: Persistent Coding Agents (MCP Integration) We built an MCP server for Claude Code and Cursor. Instead of dumping 5 whole files into the context window for a refactor, Semvec tracks the architectural invariants and past error patterns across different sessions. It gives your coding agent a persistent "Second Brain"—if it messed up a database schema in session 2, it remembers the "anti-resonance" rule in session 35 so it doesn't make the same mistake. Multi-Agent Swarms (Cortex) If you run multiple agents (like an Analyst and a Critic), they shouldn't have to read each other's 10,000-token transcripts to collaborate. With the Cortex module, agents exchange compressed StateVectorPackets and use a ConsensusEngine to merge their perspectives mathematically, sharing a global state with zero overhead. Enterprise Auditability & GDPR (Compliance Pack) If you run AI memory in production, you need to prove exactly what state the LLM acted on, and you need to be able to legally delete it. The compliance pack handles this via an append-only event store for deterministic replay, HMAC request signing, and GDPR Art. 17 "Right to be Forgotten" workflows with signed deletion certificates. The Benchmark Data: True Constant Cost: We ran a 50,000-turn stress test. While standard baseline history exploded past 75,000+ tokens, Semvec's footprint stayed flat at around ~550-625 tokens per turn. Quality goes UP: Because we strip out the noise and feed the LLM a highly concentrated "essence" of the context, blind A/B LLM-judge scores on LongBench-v2 actually increased for both small models (Llama 3.1-8B) and massive ones (gpt-oss-120B). A quick note on privacy & tracking: When I was initially designing the commercial licensing side, I experimented with an anti-abuse telemetry script to prevent automated clone-training. This was a terrible approach that compromised the local-first nature of the tool. I have completely ripped it out in v0.5.1, all versions containing it are yanked. Semvec for community users is now 100% air-gapped, local, with zero background tracking. The core engine is proprietary/patent-pending to bootstrap the project, but you can pip install the Python SDK and the MCP Server right now for free via the built-in community license. I'd love to hear your thoughts on the O(1) memory architecture vs. Prompt Caching, and if you think bounded semantic states are the future of long-running agents. Docs & Architecture: https://semvec-docs.pages.dev/ PyPI: https://pypi.org/project/semvec/ submitted by /u/scheitelpunk1337 [link] [comments]

Tried the Seedance-in-presentation use case I mentioned awhile ago — here's the actual workflow

Hey it's me again, I posted a week or two ago about the non-obvious application of Seedance 2.0. You can view the original thread here: https://www.reddit.com/r/artificial/comments/1szkpjb/seedance_20_whats_the_most_interesting_nonobvious/ The reason why I'm so interested in this scenario is because both my parents are teachers and I have seen them waste away countless hours in building slide decks for their students. More often then not, they have supplementary material to show the class so they do a lot of switching back and forth between sources, videos, etc. When I first saw the use case of embedding a Seedance video in a presentation my first thoughts were: this will greatly reduce students' attention lost from switching between teaching materials. So I did some searching and gave the web-app a test. If anyone is interested in trying it out yourself here is the link: pi.inc Conclusion: The end product is 9/0. The workflow however is about 7/10. The problem lies in the fact that you have to generate your video and your deck in two different interfaces. And you have to download your video first and then upload it back into your deck. Pi does give you a workspace, one for your decks and another for your video, but it can't pull video from said workspace. So it takes a minimum of 2 prompts and downloading/uploading to get everything done: generate video and download it generate slide and upload video What I think would be better: generate slide generate video and embed It also has GPT-image2 and you can directly create in the slide deck interface. Now why can't I do the same with Seedance 2.0? I'm not a tech person, is there an underlying difference between generating a video vs an image post process? I'm going to try out some other AI presentation tools soon, if I find anything interesting maybe I'll post again! submitted by /u/Murdon [link] [comments]

PyTorch reproduction of TensorFlow paper underperforms by 4 pp on DermaMNIST , what cross-framework issues should I check? [R]

I'm reproducing a published paper's hybrid Gabor + CNN architecture in PyTorch. The original implementation is in TensorFlow. My reproduction consistently lands ~4 pp below the paper's reported test accuracy on DermaMNIST (73-74% vs paper's 77.01%). I'd like to know which cross-framework differences are most likely to cause this gap. Ahmed et al., "A Lightweight Hybrid Gabor Deep Learning Approach", IJCV 2026 (DOI: 10.1007/s11263-025-02658-2). The architecture is a fixed Gabor filter bank front-end followed by a small CNN with one SE block, one residual block, and three FC layers. ~340k parameters total. I've already tried Different sigma_factor values (1.0 vs 1.2) and Multiple random seeds (42, 0, 123) and tried diffrent sigma valyes of the lpf and hpf channels but its didnt close the gap. please any idea on how to at least get a 76% to match the paper because i wanted to add improvements to see the diffrence, i would really appreciate it on how to fix this problem or any advice on what to do. also here is just example of one epoch i have noticed that the test accuracy is lower than the validation accuracy: im i doing something wrong [ 47/100] Train: 75.70% Val: 76.07% Best: 76.97% Loss: 0.6827 [paper] test acc = 0.7382 Code example: python class FixedGaborFrontEnd(nn.Module): def __init__(self, scales=(0.10, 0.20, 0.40), orientations=(4, 4, 4), sigma_factor=1.0, input_size=224, output_size=56): super().__init__() # Build Gabor parameters (fixed buffers, not learnable) sigmas, thetas, freqs, kernel_sizes = [], [], [], [] for f, o in zip(scales, orientations): sigma = sigma_factor / (math.pi * f) N = 2 * int(math.floor(3 * sigma)) + 1 for k in range(o): sigmas.append(sigma) thetas.append(math.pi * k / o) freqs.append(f) kernel_sizes.append(N) # ... build real/imag kernels with zero-mean + L2 normalization ... def forward(self, x): # Convert RGB to grayscale if x.shape[1] != 1: x = 0.299 * x[:, 0:1] + 0.587 * x[:, 1:2] + 0.114 * x[:, 2:3] real = F.conv2d(x, self.real_kernels, padding=self.max_kernel_size // 2) imag = F.conv2d(x, self.imag_kernels, padding=self.max_kernel_size // 2) magnitude = torch.sqrt(real ** 2 + imag ** 2 + 1e-8) lpf = F.conv2d(x, self.lpf_kernel, padding=self.lpf_pad) hpf = F.conv2d(x, self.hpf_kernel, padding=self.hpf_pad) feats = torch.cat([magnitude, lpf, hpf], dim=1) feats = F.avg_pool2d(feats, 4, 4) # 224 → 56 return feats # Standard backbone follows: SE → Conv-BN-ReLU → MaxPool → ResBlock → Dropout → GAP → FC × 3 optimizer = torch.optim.Adam(model.parameters(), lr=1e-3) scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.5 submitted by /u/Plane_Stick8394 [link] [comments]

How does Claude (with access to the law) perform compared to law-specific AI systems (like Westlaw/Lexis)? We ran a series of head to head tests

We’re now a couple of years into the AI wave, and it seems like the available legal AI technology has begun splitting down two different tracks: In one direction, there are general purpose AI systems like Claude or Chat GPT; in the other direction you have purpose-built legal AI systems like Westlaw’s AI Deep Research and Lexis Protege. We’re two active litigators (Ding and Duff) who use both Claude and Westlaw regularly. Curious to see how well the various systems perform legal research, we decided to run a series of comparison tests consisting of five prompts across all three systems. We think the results are interesting so we’ve decided to share them. By itself Claude doesn’t have access to the cases or statutes. We’ve used a connector that we built called DingDuff (it’s free for now if you supply your own Anthropic API key). As discussed below, DingDuff allows Claude to search for and retrieve cases and statutes, but the decisions about what to research or how are coming from Claude (we ran tests with and without a case law research skill file and it didn’t make a huge difference). One fascinating result of this test is it reveals how quickly Claude has improved as an AI system. These outputs were mostly generated in late April 2026 using the latest version of Claude co-work and (we think) they are very impressive. Claude could not have produced these outputs a year ago. The five prompts are made-up fact patterns designed to cover different states and different areas of law, but we tried to craft them so that they resemble real prompts we actually use. The prompts Prompt 1 Adverse Possession — Walton County, GA. Prepare a memo analyzing my client's position in a boundary dispute in Walton County, Georgia. In 1998 my client's predecessor-in-title built a barbed-wire fence intended to follow the surveyed boundary between two rural parcels. A 2024 survey revealed that the fence encroaches approximately 12 feet onto the adjoining owner's land over a 400-foot run, enclosing roughly 4,800 square feet. My client bought the property in 2011 and has continuously grazed cattle on the enclosed strip; his predecessor used it for pasture from 1998 to 2011. The record owner has paid property taxes on the disputed strip throughout. The neighbor first objected in late 2025 and has threatened ejectment. Please address: (1) whether my client can establish title by adverse possession (20-year) or prescription (7-year under color of title) under relevant Georgia statutes and case law; (2) whether tacking between predecessors is available on these facts; (3) whether the hostility element can be satisfied when the parties mutually (but mistakenly) believed the fence sat on the true line — i.e., the "mistaken boundary" line of authority; (4) the effect, if any, of the record owner's tax payments; and (5) the procedural vehicle and venue for quieting title. 2 Piercing the Corporate Veil — Single-Member Delaware LLC, Harris County forum. Please prepare a memo analyzing whether a trade creditor can pierce the veil of a Delaware LLC whose sole member is a Texas-resident individual. The LLC was formed in Delaware in 2019 to operate a single Houston-area restaurant. The sole member routinely paid personal expenses (his home mortgage, his wife's vehicle lease, his children's tuition) directly from the LLC operating account; the LLC never adopted anything beyond a one-page operating agreement, held no member meetings, and was initially capitalized with $5,000 against monthly operating expenses of roughly $80,000. My client, a produce wholesaler, is owed approximately $220,000 on open account. The LLC has ceased operations and is insolvent. Suit will be filed in Harris County. Please address: (1) whether Delaware or Texas law governs the veil-piercing analysis under Texas choice-of-law principles (internal affairs doctrine vs. substantive tort/contract characterization); (2) the substantive standards under each jurisdiction; (3) whether reverse veil-piercing is available; and (4) whether a companion Texas Uniform Fraudulent Transfer Act claim against the individual member is viable and how it interacts with the veil theory. 3 Mechanics Lien Priority — Subcontractor vs. Construction Lender, LA County. Please prepare a memo analyzing priority between my client (an HVAC subcontractor) and a construction lender on a mixed-use project in Los Angeles County. My client first furnished labor and materials on March 3, 2024, and served a 20-day preliminary notice on the owner, general contractor, and the original construction lender on March 28, 2024 (within statutory time). The original lender assigned the construction loan to a successor lender in July 2024; my client did not serve a new preliminary notice on the successor. My client last furnished work on December 15, 2024, and recorded a mechanics lien on February 10, 2025 (56 days later). The general contractor recorded a notice of completion on January 2, 2025. The s

I built a geological clock that maps Earth's 4.5 billion year history onto 12 hours

eona.earth The clock runs on your local time, so whatever time you're reading this, you're looking at a specific moment in Earth's history. At 12:06 the moon forms. At 2:45 first life appears. At 11:39 the dinosaurs go extinct. Humans appear within the last 3 seconds. I used Claude Code to build the whole thing as a single HTML file (vanilla JS, Three.js for WebGL, no build step), using a custom WebGL shader to render the globe with paleogeographic continent data, procedural clouds and atmospheric haze that evolve as you move through geological time. You can also drag the scrubber handle to move through 4.5 billion years manually, and toggle layers on and off using the controls in the top-right corner. I’m a product designer with basic HTML and CSS skills, so I know my way around an interface but otherwise this is all new territory for me. I’m on the Pro plan (which I also use during the day for work stuff) so I had to be pretty conservative with my usage. I mostly stayed within the weekly limits by being intentional with my input: short sessions, working off-peak, working outside Claude where possible, keeping it in the loop with context files, etc. Opus 4.7 had just launched when I decided to do this so I let it run with the idea for the first evening, but stopped after the initial build because it was over-engineering everything and generally making things more complicated than necessary. (One example: it had the fragment shader running 4 noise passes per pixel, every frame, at 60fps, which my devices were not happy about.) I iterated on the design in Figma, then implemented mostly with Sonnet, or Opus 4.6 when it got stuck or for more complex work. The phases of the earth were definitely the most fun. I had an initial palette that I fed to Gemini (free plan on Thinking mode) to establish a system that flexed across 14 different phases of Earth’s evolution. These approximated what might have been going on at a given moment, but were also stylised enough to help illustrate the key events along the timeline. Opus 4.6 then built me an interactive palette editor (unprompted) for adjusting colours, surfaces and clouds, which was unexpected and very impressive. It also figured out how to render the post-cryogenic snowball earth using the paleogeographic continent data: a series of maps that we shape-tweened to animate the continents as they drift through deep time. Why did I build this? I find the concept of deep time helps me maintain perspective. From a geological point of view we’re insignificant, which is a good reminder not to take things too seriously when life gets heavy. It's a privileged perspective to have. I’ve been wanting to build something like this for ages and was finally able to do it. About 2 weeks of work (mostly evenings) so far. So what’s next? Keyboard navigation to jump between events (user feedback) Scrub without spinning the globe to observe continental drift (user feedback) A future earth projection covering remaining lifespan of the planet over second 12 hour period A physical build using a Waveshare round display and a Raspberry Pi 4 Sound design to give this an auditory layer An app for watch, mobile and/or desktop Your feedback is welcome and appreciated. If the interest is there, I’ll make sure to share a follow-up post as things progress. Links Live site: eona.earth Colour lab (interactive palette editor): eona.earth/colour-lab.html Source: github.com/owen-thomas/eona-earth submitted by /u/Exciting_Alps_1457 [link] [comments]