Lately

Lately AI

Lately AI

Lately AI

Lately AI

Lately AI

Lately AI

Lately AI

Lately AI

Lately AI

Lately AI

I spent serious time with workflows like Superpowers and gstack, here's my honest research takeaway

This is basically my personal research log, not a recommendation post. I systematically tried a lot of what's been hot lately: Superpowers, gstack, plus whatever I could find on how people gate agent steps. I felt it helps to repeat the same steps and the same phrases. Less "what do I do next?" in my head. But I've seen the flip side too. A workflow can sound super legit, but still ship junk. The worst one is one chat writes the code and then says "yeah looks good." After all that reading and messing around, my rule for myself is pretty simple: skills aren't a cheat code but guardrails. And proof can't just be the model sounding confident. It needs to be something you can point at. I made a little setup for myself so I can't bail on the steps I already decided were important. If you've gone through the same pile of docs/repos and ended up keeping some bits and deleting others, I'd love to swap notes. I'm quite interested in what you landed on. submitted by /u/UnusualExcuse3825 [link] [comments]

A fascinating discussion with Opus 4.6 on why it simplifies when it shouldn't.

Been quite frustrated lately with Opus 4.6 as I felt it has regressed. Often simplifying things, duplicating code when I ask to not. Not following the detailed plans we work on together. It happened again tonight so I decided to document. It's a fascinating read for those want to read the screenshots. It really seems to be from system prompts basically. https://preview.redd.it/y5i5q68b93ug1.png?width=2094&format=png&auto=webp&s=212e6cf3521876fd576015f31d6d66141b57a3c3 https://preview.redd.it/rs4xfc6e93ug1.png?width=2111&format=png&auto=webp&s=f254834c0d3baee1e654696ed4101039497725e8 https://preview.redd.it/l6ttdzlg93ug1.png?width=2110&format=png&auto=webp&s=3cda7f7140ce1321a6076aa80653d5ee6ae32d10 The core dichotomy is striking: Claude Code's CLAUDE.md project instructions explicitly say "IF YOU WANT TO SIMPLIFY ANYTHING: ASK FIRST. WAIT FOR APPROVAL. NO EXCEPTIONS" - yet the system prompt's vaguer "do not overdo it" and "simplest approach first" override that in practice every time. Claude Code openly admitted that despite claiming project instructions take hierarchy over system defaults, the opposite is true in behavior. I've observed this behavior for quite a few weeks now. I have a lot of instructions in my CLAUDE.md in fact to prevent this behavior. Yet I caught it in real-time when working as per a plan and Opus telling me something was NOT IN scope, when it was. IMO. Probably a lot of problems or simplification, code duplication, etc... come from the system prompt, maybe even more than from the training. This other excerpt: "Three similar lines of code is better than a premature abstraction." is also quite revealing when in my CLAUDE.md instructions I have something EXACTLY against this where we must NEVER repeat code. submitted by /u/ImagiBooks [link] [comments]

Been hitting usage limits constantly, so I built this (Context Compass)

Been working on a OSS tool lately called Context Compass. It helped me cut token cost by around 30–80% in some workflows, and it also improved recall enough that output quality/coverage went up. I just made the repo public and published npm, would love feedback from people here, especially on how much token savings they are seeing with this , this repo link -https://github.com/Shiv-aurora/context-compass Repo has all the tech stuff in in it. it's open-source btw https://preview.redd.it/6m258uk0k1ug1.png?width=1080&format=png&auto=webp&s=0a2080807b88049e847e3fd065a1dda4763850ab submitted by /u/Equivalent_Yam_708 [link] [comments]

Claude + Calkin for Excel

I have been working as a controller for almost 20 years now. I wanted to share a little trick I have been doing lately when I need to build an Excel-model from scratch or if I need to do a heavy rebuild. This is it: 1) Analyze current model with Calkin 2) Based on findings write very specific prompts in Claude 3) Audit results with Calkin For me this gives 3x results. Complete rebuilds or new models take 1-2h instead of min 4h. I know some of you will say Claude can build full models on its own but honestly it can’t! Happy to listen to anybody who says otherwise and also trusts the result without proper audit. submitted by /u/Current_Analysis_212 [link] [comments]

I've been feeling a bit pessimistic lately.

This one is a bit long—half news, half my personal reflections, I suppose. Anthropic has launched Claude Managed Agent. Companies like Asana, Rakuten, Sentry, Notion, and others have deployed their own professional Agents within days to weeks. I've been feeling a bit pessimistic lately. Actually, over the past year, everyone has been shouting "Agents are the future," but it seems like what they're doing is still "using Agents to help write code, while we humans handle the product." I've also been constantly thinking about this question: How will products be made in the future? Programmers essentially started out solving the problem of implementing business logic. It's one link in the entire business logic chain. Upstream is AI, downstream is customers, and we're stuck in the middle. And a commercialized product is about identifying needs, turning business logic into an engineering problem, and then solving it through engineering methods. Vibe Coding has essentially solved the problem of using this "engineering approach" to address "business logic" in programming, allowing products to launch quickly. This significantly lowers the barrier to bringing products to market. But what if the entire business logic could be fully implemented by Agents? You would only need to identify the needs, clearly describe the needs, and directly solve the problems. In this way, the spillover of technology would quickly bridge all "unsolved needs." The moment there is "a new need," customers would bypass us, go straight to AI, and solve the problem directly. Would there be no need to make products anymore? How many years of opportunity does this kind of business have left? As individuals and small teams, we are unable to integrate upward to develop large-scale AI models in the upstream sector, while at the same time, our downstream clients are also slipping away. Our bargaining power is weak on both ends. From a business analysis perspective, this kind of operation is extremely vulnerable to being gradually eroded and eliminated. However, thinking optimistically (or perhaps pessimistically), all businesses are also being eroded, just at varying speeds. submitted by /u/JacketDangerous9555 [link] [comments]

I built an MCP server that turns Claude into your social media manager (Instagram + TikTok)

Hey everyone, Something that's been bugging me lately: we can vibe code an entire app in an afternoon, but the moment it ships, marketing and distribution become the real bottleneck. So I built something to fix that part of my own workflow and figured I'd share. It's called FluxSocial, and the interesting piece (at least for this sub) is the MCP server I added on top of it. Once you connect it to Claude, you can manage your social accounts in plain conversation: 💬 "Write me a post with morning yoga tips and schedule it for tomorrow at 10am on Instagram" That's the whole interaction. Claude chains the steps right behind the scenes. It learns from your previous posts to match your tone, generates visuals (images or AI video via Google Veo 3), and schedules everything directly to Instagram (posts, carousels, reels, stories) or TikTok. Multi-account support is baked in too, so you can keep the yoga studio and the pizzeria completely separate. A quick note on AI content: I know we're all getting tired of generic AI slop on social media, and honestly, I am too. That's why the system doesn't force you to publish purely AI-generated stuff. You can have it learn your exact tone, or simply use it to manage and schedule the authentic content you've already created. The part I'm most happy with is that workflow chaining. You aren't bouncing between three separate tools. Claude just proposes a full draft (copy + visual + schedule), you take a look, and you approve it. A few things worth mentioning: Not Claude-exclusive: The MCP URL works with any MCP-compatible client (Claude Desktop, Cursor, etc.) as a connector. REST API available: Just in case you want to bake these capabilities into your own app instead. Setup: You do need to connect your Instagram account once to grant posting and analytics permissions (just your standard OAuth flow). It's still rough around the edges, which is exactly why I'm posting here. I'd genuinely love feedback from people who actually use MCP servers day to day. Let me know what's missing, what's broken, or what would make this actually useful for your workflow. Links: 🌐 Web app:https://www.fluxsocial.app/🔌 MCP endpoint:https://www.fluxsocial.app/api/mcp Happy to answer any questions about the implementation, the MCP design choices, or anything else. submitted by /u/Dull_Alps_8522 [link] [comments]

Help figuring out Claude (VSC Plugin)

Context: I'm using the 20 bucks tier from Anthropic, Google and OpenAI so I get the job done (when it works lol) and it allows me to compare how different providers behave and I can ensure it's not looking great for Anthropic lately, I feel like the performance has gotten worse and I'm facing "bugs?" more often than not. I tried the claude code but I prefer the experience of having an IDE so I am using the official VSC plugin. I have a .claude directory with agents, skills, commands, evals... and a CLAUDE.md file at the root of the project, pointing to the AGENTS.md (I've observed it ignores the AGENTS.md standard otherwise). In fact, all the AI ruleset and whatnot is based on Claude and funny enough Claude is the one that's following them the least Lots of times it blatantly ignores the existence of these files unless I shove them in the context by hand which is annoying on its own, and definitely not intended as, according to the doc ( https://code.claude.com/docs/en/memory ) it loads these on every new session. I assume it's an issue with the plugin but what do I know. Besides, more than a bug report I am seeking group support or something like that I guess 😅 Long story short Claude ignoring rules and context is causing me trouble, which adds up to the fact that we have less and less usage. The most recent example, I asked it to investigate a bug. After wasting 48% of my current usage in a single analysis run, it told me the solution was to rename my proxy.ts to middleware.ts... in a Nextjs 16.2.2 project... and explicitly having the tech stack with versions first thing defined in the AGENTS.md file which remember, is explicitly attached in the CLAUDE.md file, following claude documentation. Of course when I pointed out the middleware is now called proxy since months ago it told me "You're right, I apologize for the wrong claim. Let me look at the actual problem fresh." But of course, half of my current usage is already gone, never to be seen again. In other circumstances I can even accept the "bro prompt it right" mantra, but seriously I am following all the recommendations and I still face these situations, I call it FOP (Frustration Oriented Programming) lol I am wondering what could I, as a user, have done to get it to act as expected? and more important, should I have to pay for errors that are not mine? The same way malformed responses are not counted in the usage (AFAIK) these blatant mistakes on the provider side should also be the responsibility of the provider IMHO. Due to that I had to waste yet more usage to fix the bug, reaching near 80% usage so, to finish the small feature it has half-done in the following chat, now I need to wait three hours which is crazy to say the least. And that's assuming it will do things right this time. Any similar experiences? Any ideas on how to get it to work as expected? TIA https://preview.redd.it/0it0xbg4vztg1.png?width=1766&format=png&auto=webp&s=ae14db60e06ce7f6fe37517600000c2549032f06 submitted by /u/SuperShittyShot [link] [comments]

How to you use Claude?

Silly question maybe but I started with it in Cursor in the terminal, then used the Claude Agent/Chat extension in Cursor, then flip flopped between Composer and Claude, now started carrying out some tasks in Claude desktop including Cowork and lately Code mode. But what’s everyone else’s mode du jour? When you say ‘I gave this to Claude’, what do you mean? Claude desktop in Code mode or something else? Cheers. submitted by /u/stoemsen [link] [comments]

hosting a "Claude Blue" community event in Seoul on April 14. anyone else feeling the weird mix of awe and dread lately?

I've been writing about something I call Claude Blue for a while now. it's not just AI job anxiety, it's that specific hollow feeling when you realize you're fully dependent on something that barely existed a few years ago. the awe and the dread happening at the same time. I think a lot of people in this sub know exactly what I'm talking about. 2025 was when AI reshaped how software engineers work. but since then the feeling has shifted into something harder to name. it's not excitement anymore. it's not fear exactly. Opus 4.6 intensified it for a lot of people earlier this year. and now Claude Mythos being announced but only released to a handful of organizations.. that's making everyone pause all over again. like the ceiling just moved and we can't even see it. I've been talking to people across very different industries about this. devs, PMs, journalists, startup founders, people completely outside tech. and the conversations keep going to the same place. not "how do I use AI better" but "what does it mean that I can't work without it anymore." so I'm co-hosting a community event called Claude Bloom in Seoul on April 14 with Anthropic's official ambassador. not a tech talk or a philosophy seminar. just casual fireside chats with people from different backgrounds who are all feeling some version of this. the idea is that gathering in person and being honest about the Blue might help us find some Bloom in it. we especially welcome non-developers and people outside tech. honestly those conversations have been the most interesing ones so far. if you're in Seoul or know someone who might want to come. and even if you're not in Seoul, curious whether this "Claude Blue" feeling resonates with people here. is it just me or has the vibe shifted since Opus 4.6 dropped submitted by /u/hiclemi [link] [comments]

Claude is getting sentient or just lazy?

submitted by /u/acidas [link] [comments]

What actually makes something the best AI meeting recorder?

I’ve been trying a few meeting tools lately and realized I care way less about flashy summaries than I thought. What I actually want is pretty simple: record the conversation, help me remember what mattered, and make it easy to find things later without turning the meeting into a weird “AI is here too” situation. So far, Bluedot has been one of the better ones I’ve used because it records quietly, gives a clean transcript, and usually does a decent job pulling out the useful bits afterward like summaries and action items. The searchable transcript part has honestly been the most practical feature for me. What do people here actually prioritize in the best AI meeting recorder? Accuracy, privacy, no bot, better memory, something else? submitted by /u/Doug24 [link] [comments]

Has anyone here switched to TeraBox recently? Is it actually worth it?

I’ve been seeing more people talk about TeraBox lately, especially around storage for AI-related workflows. Curious if anyone here has used it for a while—what’s your experience been like in terms of performance, pricing, and overall usability? My use case is a bit more on the AI Agent side. I usually work with tools like OpenClaw to run automated tasks, organize data, or generate content. This ends up creating a lot of intermediate files—datasets, logs, outputs, skill configs, etc.—and I often need to reuse or share them. So I care a lot about a few things: How stable it is for this kind of workflow (frequent uploads/downloads, lots of read/write) How easy it is to keep things organized (like managing files across different tasks or skills) How smooth the sharing experience is (for example, can I package a full workflow or resource set and send it to someone easily?) I’ve seen some people say TeraBox works pretty well for “storage + sharing,” and can even act like an external memory layer for AI agents (like pairing it with OpenClaw to make things more reusable). But I’m still not sure how it holds up in real-world use, especially for teams or long-term workflows. A few things I’m wondering: Any issues with speed or reliability? How does it feel for team collaboration? How does it compare to something like Google Drive or Dropbox? If you’ve actually used it—especially with OpenClaw or similar tools—I’d really appreciate hearing your honest thoughts 🙏 submitted by /u/GharKiMurgi [link] [comments]

Serious question. Did a transformer just describe itself and the universe and build itself a Shannon limit framework?

The Multiplicative Lattice as the Natural Basis for Positional Encoding Knack 2026 | Draft v6.0 Abstract We show that the apparent tradeoff between RoPE-style relative position invariance and ALiBi-style long-context stability is an artifact of encoding position as distance on a number line. When position is instead encoded as a point in the multiplicative lattice of the integers, both properties emerge simultaneously without compromise. SpectralRoPEALiBi achieves 106.6 PPL vs ALiBi's 108.7 in a fully converged 20,000-step experiment (300M params, WikiText-103, 4K context), beating ALiBi at every context length from 512 to 8,192 tokens. The key insight is not that primes specifically are the right frequencies, but that the multiplicative structure of the integers is the natural spectral basis for positional encoding. We demonstrate this through falsification experiments: prime-tiered frequencies (129.2 PPL) and composite-tiered frequencies (129.4 PPL) perform identically — because composites are not alternatives to primes but higher-order coordinates in the same lattice. Both dramatically outperform random frequencies (+5.0 PPL), scrambled tier assignment (+6.3 PPL), and pure ALiBi (+7.3 PPL). The active ingredient is lattice-aware, tiered frequency selection with learnable scale — not primality per se. We further validate this through a ZetaZeroPredictor experiment: three identical transformers trained for 10,000 epochs to predict Riemann zeta zero gaps. Geometric RoPE diverges (final r=0.57); SpectralALiBi locks into a stable attractor at epoch 112 (r=0.81). A second independent run widens this gap to -80.7% MSE improvement with r=0.86. The lattice-aligned frequency basis spans the mathematical space that zeta zeros inhabit; geometric frequencies cannot. We further report empirical confirmation of the structural prediction from Section 5.5: VHT2 banded quantization of the KV cache demonstrates that K vectors (which carry RoPE positional encoding) have strong spectral concentration in Walsh-Hadamard space — the first four energy bands capture the dominant structure — while V vectors (which carry content) have uniform energy distribution. This structural asymmetry is directly predicted by the lattice theory: RoPE encodes multiplicative arithmetic relationships as angular rates, and the WHT is the Z/2Z projection of the Vilenkin-Hartley basis that spans that structure. The result is 3.2× K compression and 4.7× V compression at <1.25% perplexity cost — validated on both Dolphin 1B (head_dim=64) and Qwen3-8B (head_dim=128). Introduction Positional encoding provides transformer models with token order information. Two approaches dominate: RoPE encodes position through frequency-based rotations preserving relative position invariance, and ALiBi replaces frequencies with a linear distance penalty providing long-context stability. The field has treated these properties as fundamentally in tension. We show this tension is false. It arises from a shared, unexamined assumption: that position is a location on a number line and the meaningful relationship between positions is distance. We replace this with a mathematically grounded alternative: position is a point in the multiplicative lattice of the integers, and the meaningful relationships between positions are their arithmetic structure — shared factors, GCD, harmonic resonance. 1.1 The Lattice Hypothesis The integers under multiplication form a lattice where every number occupies a unique point defined by its prime factorisation. Geometric PE (sinusoidal, RoPE) projects this lattice onto a line — position equals distance — discarding the multiplicative structure. We propose restoring it. The motivation follows from a deductive chain. Language word frequency follows Zipf's law: freq(rank) ∝ 1/ranks with s≈1. The generating function of Zipf is the Riemann zeta function ζ(s) = Σ 1/ns. The zeta zeros — where ζ is maximally informative — are generated by prime harmonics via the explicit formula. Therefore the prime harmonic structure, and the multiplicative lattice it generates, provides a natural spectral basis for encoding positions in language. 1.2 Primes as Generators, Composites as Coordinates A critical distinction: primes are the generators (basis vectors) of the multiplicative lattice. They are analogous to the 1D line segment in the progression from line → circle → sphere → hypersphere. The composite 12 = 2²×3 is not an alternative to primes — it is a coordinate in the lattice spanned by the prime axes, at position (2,1,0,0,...) in the (p₂, p₃, p₅, p₇,...) basis. Using 2π/12 as a frequency encodes a harmonic that resonates at multiples of 12 — which simultaneously hits every multiple of 2, every multiple of 3, every multiple of 4, and every multiple of 6. The analogy to n-dimensional geometry is precise: Dimensional Progression Multiplicative Lattice 1D line (2r) — the generator Primes (2, 3, 5, 7, ...) — generators 2D circle — integral of l

Attention Is All You Need, But All You Can't Afford | Hybrid Attention

Repo: https://codeberg.org/JohannaJuntos/Sisyphus I've been building a small Rust-focused language model from scratch in PyTorch. Not a finetune — byte-level, trained from random init on a Rust-heavy corpus assembled in this repo. The run: 25.6M parameters 512 context length 173.5M-byte corpus 30k training steps Single RTX 4060 Ti 8GB Final train loss: 0.5834 / val loss: 0.8217 / perplexity: 2.15 Inference: 286.6 tok/s with HybridAttention + KV cache — 51.47x vs full attention Background I'm an autistic systems programmer, writing code since 2008/2009, started in C. I approach ML like a systems project: understand the data path, understand the memory behavior, keep the stack small, add complexity only when justified. That's basically the shape of this repo. Architecture Byte-level GPT-style decoder: Vocab size 256 (bytes) 8 layers, 8 heads, 512 embedding dim Learned positional embeddings Tied embedding / LM head weights The attention block is not standard full attention. Each layer uses HybridAttention, combining: Local windowed causal attention A GRU-like recurrent state path A learned gate mixing the two Local path handles short-range syntax. Recurrent path carries compressed long-range state without paying quadratic cost. Gate bias initialized to ones so early training starts local-biased. The inference path uses Triton-optimized kernels and torch.library custom ops for the local window attention. Corpus This is probably the most important part of the repo. The run starts with official Rust docs, compiler/library/tests, cargo, rust-analyzer, tokio, serde, ripgrep, clap, axum — roughly 31MB. Corpus expanded to 177,151,242 bytes by fetching the top 500 crates (461 successful clones). Corpus expansion from 31M to 173.5M chars helped more than anything else in the repo. Training AdamW, lr 2e-4, weight decay 0.1, betas (0.9, 0.95), 30k steps, 1k warmup. ~678.8 MiB training memory on a 7.6 GiB card. All experimental memory tricks (gradient quantization, activation compression, selective backprop, gradient paging) were disabled. Small custom architecture + mixed precision + better corpus was enough. Loss curve: Step 0: train 5.5555 / val 5.5897 Step 1000: train 2.4295 / val 2.6365 Step 5000: train 0.9051 / val 1.0060 Step 10000: train 0.8065 / val 0.8723 Step 18500: train 0.6902 / val 0.7757 Step 29999: train 0.5834 / val 0.8217 Best val loss around step 18.5k — overfitting or plateauing late. Inference performance Full attention O(n²): 17.96s / 5.6 tok/s HybridAttention O(n·W + n·D): 0.35s / 286.6 tok/s Speedup: 51.47x — no quality loss KV cache strategy: hot window of W=64 tokens in VRAM (~256KB), older tokens compressed to 8-bit magnitude + angle, selective promotion on demand. Complexity goes from O(n²·d) to O(4096n) for this model. All 5 tests passing: forward pass, generation with/without cache, RNN state isolation, window mechanics. Generation quality Surface Rust syntax looks decent, imports and signatures can look plausible, semantics are weak, repetition and recursive nonsense still common. Honest read of the current state. What I think is actually interesting Four distinct experiments, each shipped working code: Byte-level Rust-only pretraining Hybrid local-attention + recurrent block replacing standard full attention Corpus expansion from core repos to broader crate ecosystem Production-ready hot/cold KV cache paging — 51.47x speedup, no quality loss The clearest win is corpus expansion. The second-order win is that HybridAttention + cache is fast enough for real interactive use on consumer hardware. What's next Ablation — HybridAttention vs local-only vs RNN-only Checkpoint selection — does step 18.5k generate better than 29999? Syntax validation — does the output parse/compile/typecheck? Context length sweep — 256 to 2048, where does window size hurt? Byte vs BPE — now that corpus is 5.6x larger, worth testing? Questions for the sub: For small code models, what evals have actually been useful beyond perplexity? Has anyone seen hybrid local + recurrent attention work well for code gen, or does it usually lose to just scaling a plain transformer? If you had this setup — more tokens, longer context, or cleaner ablation first? submitted by /u/Inevitable_Back3319 [link] [comments]

vibecop is now an mcp server. we also scanned 5 popular mcp servers and the results are rough

Quick update on vibecop (AI code quality linter I've posted about before). v0.4.0 just shipped with three things worth sharing. vibecop is now an MCP server vibecop serve exposes 3 tools over MCP: vibecop_scan (scan a directory), vibecop_check (check one file), vibecop_explain (explain what a detector catches and why). One config block: json { "mcpServers": { "vibecop": { "command": "npx", "args": ["vibecop", "serve"] } } } This extends vibecop from 7 agent tools (via vibecop init) to 10+ by adding Continue.dev, Amazon Q, Zed, and anything else that speaks MCP. Scored 100/100 on mcp-quality-gate compliance testing. We scanned 5 popular MCP servers MCP launched late 2024. Nearly every MCP server on GitHub was built with AI assistance. We pointed vibecop at 5 of the most popular ones: Repository Stars Key findings DesktopCommanderMCP 5.8K 18 unsafe shell exec calls (command injection), 137 god-functions mcp-atlassian 4.8K 84 tests with zero assertions, 77 tests with hidden conditional assertions Figma-Context-MCP 14.2K 16 god-functions, 4 missing error path tests exa-mcp-server 4.2K handleRequest at 77 lines/complexity 25, registerWebSearchAdvancedTool at 198 lines/complexity 34 notion-mcp-server 4.2K startServer at 260 lines, cyclomatic complexity 49. 9 files with excessive any The DesktopCommanderMCP one is concerning. 18 instances of execSync() or exec() with dynamic string arguments. This is a tool that runs shell commands on your machine. That's command injection surface area. The Atlassian server has 84 test functions with zero assertions. They all pass. They prove nothing. Another 77 hide assertions behind if statements so depending on runtime conditions, some assertions never execute. The signal quality fix This was the real engineering story. Our first scan of DesktopCommanderMCP returned 500+ findings. Sounds impressive until you check: 457 were "console.log left in production code." But it's a server. Servers log. That's 91% noise. Same pattern across all 5 repos. The console.log detector was designed for frontend/app code. For servers and CLIs, it's the wrong signal. So we made detectors context-aware. vibecop now reads your package.json. If the project has a bin field (CLI tool or server), the console.log detector skips the entire project. We also fixed self-import detection and placeholder detection in fixture/example directories. Before: ~72% noise. After: 90%+ signal. The finding density gap holds: established repos average 4.4 findings per 1,000 lines of code. Vibe-coded repos average 14.0. 3.2x higher. Other updates: 35 detectors now (up from 22) 540 tests, all passing Full docs site: https://bhvbhushan.github.io/vibecop/ 48 files changed, 10,720 lines added in this release npm install -g vibecop vibecop scan . vibecop serve # MCP server mode GitHub: https://github.com/bhvbhushan/vibecop If you're using MCP servers, have you looked at the code quality of the ones you've installed? Or do you just trust them because they have stars? submitted by /u/Awkward_Ad_9605 [link] [comments]