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/ ? ! $
2026-02-14 Signals
W55 NVFP4/FP4 quantization for consumer GPU inference ▸

AdaLLM implements NVFP4-first inference on RTX 4090 with FP8 KV cache, RedFire-Image-Edit ships FP8/NVFP4 quants, and NVIDIA confirms FP4 pre-training for Nemotron3 — FP4 is moving from inference hack to first-class training format.

convergence
10/35 implementation
25/30 engagement
6/15 significance
14/20

NVIDIA confirming FP4 pre-training for Nemotron3 (H1 2026) plus community NVFP4 inference on Ada Lovelace GPUs — next bottleneck is FP4 KV-cache accuracy loss at long contexts, currently worked around with FP8 KV.

3 sources
W55 Open-source real-time TTS on consumer hardware ▸

Qwen3-TTS.cpp achieves 4x speedup over PyTorch with ~2GB memory for a 0.6B model, and KaniTTS2 (400M params) runs in 3GB VRAM with voice cloning — two independent open-source TTS implementations targeting real-time conversational use on consumer GPUs.

convergence
10/35 implementation
25/30 engagement
8/15 significance
12/20

Qwen3-TTS.cpp hits 4x speedup via GGML and KaniTTS2 runs at 400M/3GB VRAM — next bottleneck is streaming first-token latency for conversational turn-taking, not yet benchmarked in either release.

3 sources
W55 LLM censorship removal via weight ablation ▸

Heretic 1.2 introduces Magnitude-Preserving Orthogonal Ablation for derestriction with 70% lower VRAM usage via quantization, 306 upvotes and 1000+ users in 3 months.

convergence
10/35 implementation
25/30 engagement
12/15 significance
8/20
1 sources
W54 Local LLM-powered coding agent workflows ▸

A 169-upvote thread collects local vibe-coding experiences across models, while a separate finding reveals Claude Code reprocesses full prompts every request when used with local models — local coding agents work but have prompt-caching and template friction.

convergence
10/35 implementation
25/30 engagement
9/15 significance
10/20

Claude Code's full prompt reprocessing on every request with local models (due to x-anthropic cache headers) wastes compute — next step is local inference servers implementing prompt-cache-aware session management.

2 sources
W53 Optimizing Qwen3-Next inference in llama.cpp ▸

ggerganov's PR #19375 optimizes Qwen3-Next graph for faster t/s, a JSON parser fix addresses OpenCode compatibility, and users compare the 60B distilled model to the full Qwen coder — active convergence on making Qwen3-Next usable in llama.cpp.

convergence
10/35 implementation
25/30 engagement
8/15 significance
10/20

Qwen3-Next graph optimization PR is in progress with multiple companion fixes — remaining bottleneck is chat template incompatibilities breaking tool-calling and structured output.

3 sources
W51 FireRed-Image-Edit open-source release ▸

FireRed-Image-Edit 1.0 model weights released on HuggingFace with 236 upvotes and 61 comments, indicating strong community interest in open image editing models.

convergence
10/35 implementation
25/30 engagement
9/15 significance
7/20
1 sources
2026-02-14 Tracking
W47 Flux 2 Klein detail preservation and anatomy control ▸

One user reports unsolved anatomical deformities in Flux 2 Klein 9B distilled img2img, while another claims to have found specific layer settings that preserve original details — community is reverse-engineering which transformer layers control fidelity vs. editability.

convergence
10/35 implementation
25/30 engagement
5/15 significance
7/20
2 sources
W46 MiniMax M2.5 local GGUF quantization and serving ▸

Users benchmark M2.5 on dual RTX 6000 Pros, discuss 4-bit GGUF quant options for 128GB RAM + 16GB VRAM systems, and share usage experiences — community is actively figuring out optimal quant/hardware configs for this MoE model.

convergence
10/35 implementation
25/30 engagement
3/15 significance
8/20
3 sources
W45 Small LLM tool-calling capability evaluation ▸

Round 2 benchmark tests 21 small LLMs on tool-calling judgment with 60 upvotes and 35 comments — systematic evaluation of which sub-30B models can reliably decide when to invoke tools.

convergence
10/35 implementation
25/30 engagement
2/15 significance
8/20
1 sources
W45 Speech-to-speech translation without aligned data ▸

Kyutai releases Hibiki-Zero, a 3B parameter simultaneous speech-to-speech translation model using GRPO reinforcement learning without word-level aligned data.

convergence
10/35 implementation
25/30 engagement
0/15 significance
10/20
1 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.