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2026-02-15 Signals
W78 VRAM constraints for local LoRA training on mid-range GPUs ▸

Users training LoRA on 5060 Ti 16GB report slow iteration times; MirrorMetric (113 upvotes) provides local evaluation tooling for character LoRAs; Unsloth at 52K stars claims 2x speedup with 70% less VRAM; LlamaFactory at 67K stars unifies fine-tuning for 100+ models.

convergence
12/20 implementation
30/30 engagement
20/20 significance
16/30

LoRA training on 16GB GPUs works but iteration speed is the pain point — MirrorMetric addresses evaluation but the remaining bottleneck is automated hyperparameter search within VRAM budgets.

4 sources
2026-02-15 Tracking
W68 Agentic coding tools requiring up-to-date documentation context ▸

Context7 MCP server (46K stars) provides live documentation to LLM code editors; everything-claude-code (46K stars) collects battle-tested agent configs; Continue (31K stars) and OpenHands (68K stars) push agentic coding with MCP compatibility.

convergence
6/20 implementation
30/30 engagement
20/20 significance
12/30
4 sources
W67 Running large MoE models locally on consumer hardware ▸

MiniMax-2.5 (230B params, 10B active) now runs locally with 200K context, ik_llama.cpp fork provides faster prompt processing, and llama.cpp updates push Qwen3-Coder-Next from 80 to 130+ tok/s on consumer GPUs.

convergence
12/20 implementation
25/30 engagement
11/20 significance
19/30

MiniMax-2.5 at 230B/10B-active runs locally in bf16 needing ~460GB — next bottleneck is quantization quality for MoE routing layers where current GGUF quants degrade expert selection.

5 sources
W64 RAG engine and graph-based retrieval framework proliferation ▸

RAGFlow (73K stars), Microsoft GraphRAG (31K stars), LlamaIndex (47K stars), and AnythingLLM (55K stars) all trending simultaneously — no consolidation visible, all have hundreds of open issues.

convergence
6/20 implementation
30/30 engagement
20/20 significance
8/30
4 sources
W64 LLM serving frontends converging on Ollama-compatible API ▸

Ollama (163K stars), Open-WebUI (124K stars), Jan (40K stars), LocalAI (43K stars), and LiteLLM (36K stars) all provide local or proxied LLM serving with OpenAI-compatible APIs, creating a fragmented but interoperable ecosystem.

convergence
6/20 implementation
30/30 engagement
20/20 significance
8/30
5 sources
W61 Web scraping and data extraction pipelines for LLM ingestion ▸

Firecrawl (83K stars) converts websites to LLM-ready markdown; browser-use (78K stars) automates browser tasks for AI agents — both address the bottleneck of getting structured web data into LLM context.

convergence
2/20 implementation
30/30 engagement
20/20 significance
9/30
2 sources
W57 NVIDIA DGX Spark CUDA software compatibility failures ▸

Week-long testing of DGX Spark reveals terrible CUDA and software compatibility despite the CUDA ecosystem being the purchase motivation; user returning the device.

convergence
8/20 implementation
25/30 engagement
10/20 significance
14/30
1 sources
W53 Video-to-video translation via single-pass LoRA without masking ▸

LTX-2 Video Translation LoRA dubs English video to French in one pass with no masking or voice-cloning, scoring 143 upvotes and 94% upvote ratio.

convergence
8/20 implementation
25/30 engagement
7/20 significance
13/30
1 sources
W45 Quantization format comparison for large coding models (Q4KXL vs MXPF4) ▸

Users comparing Q4KXL vs MXPF4 GGUF quants for Qwen3-Code-Next (MXPF4 is smaller but quality unclear); REAP variants for MiniMax-M2.5 appearing on HuggingFace with users testing different quant levels.

convergence
8/20 implementation
25/30 engagement
1/20 significance
11/30
2 sources
FAQ
What is HiddenState?

A daily briefing that scrapes 8 source types across the ML ecosystem, filters out the noise, and clusters what remains by technical mechanism — not topic.

Most ML news is recycled press releases. HiddenState watches for convergence: when multiple independent sources start working on the same bottleneck, something real is happening. Everything else is noise.

The top 10 mechanisms are ranked by W-index and split into Signals (strongest evidence) and Tracking (early signals worth watching) at the largest natural score gap.

What is W-index?

A 0–100 score measuring signal strength. Higher = more evidence that something real is happening.

ComponentMaxWhat it measures
Convergence35How many independent sources report this. Single source = 0 — unless it links to working code, which counts as a second data point.
Implementation30Evidence of working code. GitHub repo = 30. HuggingFace model = 20. Paper only = 0.
Engagement15Upvotes, stars, points. Capped low so hype can't inflate the score.
Significance20Clustering model's assessment of technical importance.

W60+ strong — W25-59 moderate — W<25 early/weak

Code beats vaporware. A shipped GitHub project with 3 sources will always outscore a hyped paper with 500 Reddit upvotes but no implementation.

Who are our sources?
SourceWhat we pull
arxivPreprints from cs.LG, cs.CL, cs.AI, cs.CV, stat.ML — the raw research firehose
Redditr/MachineLearning, r/LocalLLaMA, r/StableDiffusion, r/MLOps — practitioner signal
GitHubTrending ML repos with 50+ stars — implementation evidence
Hacker NewsML-related posts with 15+ points — cross-domain attention
HuggingFaceTrending models + watched quantizers (bartowski, MaziyarPanahi, LoneStriker)
OpenReviewTMLR + NeurIPS workshops — peer-reviewed & bleeding-edge
Twitter9 curated accounts (akhaliq, karpathy, srush, fchollet, etc.)
Papers w/ CodeTrending papers with implementations — community-vetted research
RSS BlogsLilian Weng, Chip Huyen, Eugene Yan, Simon Willison, Interconnects, Latent Space, Netflix Tech + PyTorch & HF blogs

Items that appear across multiple sources score higher. Single-source items start at zero convergence.

Signals vs Tracking — what's the difference?

Both sections show real signals. Up to 10 mechanisms are sorted by W-index and split at the largest natural score gap — Signals are above the gap, Tracking below. The split point changes daily based on the data; tied scores always land on the same side.

Tracking does not mean bad, unimportant, or wrong. It usually means a signal has fewer independent sources so far, or lacks public code — things that can change overnight. Some of the most consequential developments start in Tracking before the rest of the ecosystem catches up.

Likewise, a high W-index does not mean research is good, correct, or worth adopting. W-index measures visibility and convergence across sources, not quality. A flawed paper that gets widely discussed will score higher than a brilliant one nobody has noticed yet.

HiddenState is a detection tool, not an endorsement. It tells you where activity is clustering — what you do with that is up to you. Nothing here should be read as a recommendation, ranking of merit, or judgement on any researcher's work.

What does noise rejection mean?

Of all items collected, only 10 make it to the final briefing. The rejection rate is the percentage that got cut.

Filtering happens in three stages:

StageWhat gets cut
Pre-filterShort abstracts, low-engagement posts, duplicates across sources
ClusteringItems that don't converge on a shared mechanism with other items
RankingClusters below the top 10 by W-index

A 99% rejection rate means 99 out of 100 items were noise. That's the point — most ML news doesn't matter on any given day.

Privacy
Data collection

None. HiddenState collects no personal data, no email addresses, no IP logs, no usage analytics, and no telemetry of any kind.

Cookies & tracking

Zero cookies. No first-party, no third-party, no session cookies, no tracking pixels.

The only client-side storage is localStorage for your theme preference (dark/light). This never leaves your browser and contains no identifying information.

External requests

Pages load zero external scripts, fonts, stylesheets, or analytics. Everything is self-contained. The only outbound link is to Ko-fi if you choose to click it.

Data sources

HiddenState monitors 9 distinct public data streams (ArXiv, GitHub, Reddit, etc.) to detect cross-platform convergence. We do not use private user data; we only analyze what the community has already published.