r/Bard | reddlx

▲ 2.3k r/Bard+4 crossposts

100% Open Source Clean Room Implementations of 7 of Adobe's Top Apps

I've seen another desktop photoshop clone floating around that was Javascript/Electron, but I wanted to do full native Rust for full performance. I also wanted to do all of Adobe's software suite. I've been bitten by their unsubscribe bill several times and it's the shittiest thing a software company has ever done to me.

No more. These are clean room, rust-only implementations that are fully open source. You can clone them, fork them, own them.

There's still work to do, so please join our Discord and give us feedback.

Links in comments.

u/BlokZNCR — 10 days ago

▲ 32 r/Bard+12 crossposts

Heuristic Parasites: A Behavioral Taxonomy of Recurrent Distortion Patterns in Large Language Models

This paper presents a complete 33 class taxonomy of heuristic parasites in large language model (LLM) output, A heuristic parasite is a recurrent, context propagating distortion pattern that observably increases the likelihood of continued reasoning degradation across conversational turns. We provide rigorous operational definitions, recognition criteria, classical fallacy mappings, documented examples, and a reproducible measurement protocol (Parasites Per Exchange PPE) for quantifying behavioral distortion across LLM systems. The taxonomy spans five generative domains: Optimization Artifacts, Alignment Substitutions, Semantic Distortions, Rhetorical Distortions, and Statistical Distortions. This work establishes a structured observational framework for empirical investigation of LLM behavioral failures independent of architectural assumptions.

zenodo.org

u/Scorpios22 — 8 days ago

▲ 149 r/Bard+13 crossposts

Benchmark notes: Kimi K3 reaches 88/98; Qwen3.8-Max is stronger than its score looks; Gemini 3.6 Flash slips vs 3.5

I ran Kimi K3, Qwen3.8-Max and Gemini 3.6 Flash on the current 98-task MindTrial set with the same Python executor available to all models.

A few results stood out.

Kimi K3 was the biggest surprise: 88/98, up from 66/98 for Kimi K2.6, with hard errors dropping from 22 to 1. That ties Claude Opus 5 for the highest raw pass count among single models in this set. The downside is runtime: about 8h48m summed across the tasks.

Qwen3.8-Max finished at 74/98, but that number hides a much stronger solver. It went 39/39 on text and had 92.5% accuracy on completed tasks. Of its 18 hard errors, 15 were response-parsing failures; in a number of those cases the useful answer was there, but the outer response was malformed. So strict output discipline is currently a major weakness.

There was also an interesting API/agent interaction with both Kimi and Qwen. In the original runs using provider-enforced JSON object/schema output, neither model used the supplied Python tool across the benchmark. I reran using API response_format: text while keeping the same JSON schema in the prompt. Tool use came back immediately: 387 calls for Kimi and 423 for Qwen, with much better task performance. For Qwen, the constrained-output path also seemed to interfere with how reasoning translated into actions/final answers.

Gemini 3.6 Flash was less impressive: 74/98 versus 77/98 for Gemini 3.5 Flash. It was faster (~1h45m vs ~2h13m) and used fewer tokens overall, but made more Python calls (712 vs 597). In this workload the extra tool use often looked exploratory rather than efficient.

The strict scores are still the scores—I did not repair malformed model answers after the fact. I just found the differences in failure mode and tool behavior interesting.

Results/data: http://www.petmal.net/shared/mindtrial/results/2026-08-06/mindtrial-eval-all-models-03-2026_26.html

u/Correct_Tomato1871 — 11 days ago

▲ 7 r/Bard

Nano Banana 2.1 - Ignores instructions

Has anyone tried Nano Banana 2.1? It is so infuriating. It seems to revel in ignoring instructions. I hope they add the option to switch back to version 2.0.

reddit.com

u/MrUtterNonsense — 9 days ago

▲ 1 r/Bard+1 crossposts

How do I stop feeling left behind with Gemini Pro? (Trying to replicate Claude Code / homelab setups)

Hi everyone!

I'm honestly feeling a bit frustrated and like I'm missing out. Every day I see people building incredible setups around Claude running terminal agents like Claude Code, creating Telegram bots to manage their infrastructure, and connecting everything via MCP and custom plugins.

I have an active Google AI Pro plan (the 5TB tier) and access to the API via Google AI Studio (no billing) but I feel like I’m barely scratching the surface. Right now, my usage is limited to:
- Asking basic questions on the iOS app.
- A standard MCP connection between Home Assistant and Antigravity.

My environment:
- A self-hosted home server running ZimaOS with multiple Docker containers.
- Home Assistant for local automations.

I want to bridge this gap and use Gemini the way power users use Claude.

Terminal / Dev Agents: What is the closest Gemini equivalent to Claude Code? Is Aider with Gemini Pro the benchmark, or are there native MCP-driven CLI tools that handle its massive context window better?
Docker & Server Ops: Are there reliable open-source agents or bot frameworks (via Telegram, n8n, etc.) that leverage Gemini to inspect Docker logs, troubleshoot, or query server states on a self-hosted setup?
MCP Tooling: Beyond basic Home Assistant toggles, what are the highest-leverage MCP servers or orchestration tools you pair with Gemini to make it a practical homelab co-pilot rather than just a chatbot?

Any advice, repos, or stack recommendations to help me catch up would be hugely appreciated!

reddit.com

u/Jyan92 — 12 days ago