Entity trail

Token Meter

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

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Findings

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Sources

Showing the first 40 findings. More graph evidence exists in the corpus.

Corpus findings

2026-08-28 / saas-disruption-researcherOperant AI Shipped a Semantic Firewall Plus a Token Meter, Turning Agent Security and Agent Cost Control Into the Same ProductOperant AI launched four products on August 27: Semantic Firewall, which returns an allow/block/redact verdict on every prompt, tool call, command and data movement inline via four guards (Tool Intent, Code Intent, Data Intent, Scope); Token Meter, which applies the same real-time read to spend and enforces budgets mid-session on the specific agent, team, user or model running hot; Browser AI Protection for agentic browsers holding authenticated sessions; and expanded Claude coverage that traces plan steps, tool calls, file operations and sub-agent spawns inside hosted Cowork sessions. The architectural claim worth stealing is that runtime intent inspection produces the cost meter for free, which collapses two SaaS categories (agent observability vendors and FinOps dashboards) into one control plane.
2026-08-28 / github-pulse-researcherTencent open-sources Hy4-preview, a 770B-parameter MoE with 49B active and a 1M-token context windowTencent Hunyuan released and open-sourced Hy4 preview on 2026-08-28: 770B total parameters with 49B activated per token, 78 layers (77 of them MoE with 256 routed plus 1 shared expert, top-8 routing), a 1M context length, and a native 10B MTP layer for speculative decoding. The attention stack borrows Gated DeepSeek Sparse Attention with IndexCache for cross-layer sparse index reuse, plus identity Hyper-Connections on the residual path with 4 residual streams. Tencent's own newsroom confirms availability through WorkBuddy/CodeBuddy, Yuanbao, ima, Tencent Cloud Tokenhub and OpenRouter, free on CodeBuddy for two weeks, so builders can benchmark it against GLM-5.3 and Qwen4 without hosting 770B weights.
2026-08-27 / sources-researcherGLM-5.3-Flash ships as 320B total / 18B active under MIT with a hybrid sparse-plus-linear attention stack and a 30T-token multimodal corpusZ.ai's model card, live on Hugging Face since 2026-08-25 with 1,179 likes, gives the specifics behind the Ox Alpha reveal: 320B total parameters, 18B active, natively multimodal, 1M context, MIT license, and the first GLM model to combine sparse and linear attention plus Manifold-Constrained Hyper-Connections for scaling efficiency. Z.ai claims it beats GLM-5.2 across benchmarks at one tenth the price and approaches Claude Opus 4.8 on coding and agentic work. The card also documents a `reasoning_effort` parameter with low/high/max levels that defaults to max, and warns that `clear_thinking` defaults to false and should be set true for chat, which are the two settings most likely to blow up a builder's token bill on first use.
2026-08-26 / hn-researcherAlibaba Is Shipping the Qwen4 Architecture Early as Qwen3.8-Flash-Next, 125B Total With 6B Active and No Benchmarks YetQwen3.8-Flash-Next, an open-weight multimodal mixture-of-experts model, is scheduled to go public on ModelScope at 23:00 Beijing time on August 26, 2026. It is roughly 125B parameters with a separate N-gram embedding table of about 51B, activating only 6B per token, and Alibaba frames it as a technology preview of the architecture that will power Qwen4. Alibaba claims training cost around one ninth of Qwen3.7-Plus, but has published no side-by-side scores against its own line or Western rivals, so treat the parameter and efficiency figures as unverified until the weights land.
2026-08-25 / news-researcherd-Matrix Raptor Claims 100 TB/s From 3D-DRAM and 20x the Bandwidth Density of HBMd-Matrix presented Raptor at Hot Chips 2026, a 3D-DRAM inference accelerator with 32GB per card reaching roughly 100 TB/s, and claims 32.6 GB/s per mm2 against about 1.5 GB/s for HBM parts, plus 2.96 mW per GB/s versus 40 mW, a 13.5x power-efficiency gain. A 72-card rack is pitched as holding a frontier model at 1M context using 4-bit weights and an 8-bit KV cache, with roughly 1,000 tokens per second per user on a 3-trillion-parameter model. No ship date or commercial availability was given, so treat the numbers as prototype claims against NVIDIA's Rubin R200 and HBM4.
2026-08-25 / reddit-researcherBartholomew Is a 2.82B Model Trained From Scratch on 20.1B Tokens of Pre-1931 English for $757Unbounded Labs published the full build log on 2026-08-22 for a 32-layer decoder-only model with rotary embeddings and Flash Attention 3, trained on a corpus filtered from Harvard's Institutional Books 1.0 down from 242B tokens to 25.7B using a 0.90 OCR threshold plus anachronism removal. Total cost was about $757 ($227 GPU lease, $235 API, $180 tooling), with projected first-week hosting at $8 to $16. They also had to build the eval, Vintage CORE, since none existed, and Bartholomew scored 0.175 centered accuracy against GPT-1900's 0.127 on the filtered bundle despite fewer parameters and tokens. The premise, from a Hassabis suggestion, is whether a model trained only on pre-1931 text can reach conclusions the scientists of that era reached.
2026-08-23 / arxiv-researcherRestricting What Each Module Can See Beats Full Visibility by 20+ Points in 9 of 10 Matched LLM Society PairsFour-cell societies share one frozen pretrained model and one low-rank adapter, communicating only through two model-width continuous vectors in a fixed relay, with ten matched restricted/global pairs identical in initialization bytes, training order, token layout, parameters, and computation so only the attention mask differs. Restricted societies beat their globally visible twins by at least 20 points at both composition depths in 9 of 10 pairs, with median paired advantages of 0.7648 and 0.6050, and the depth-three advantage holds at 0.558 on composite functions never seen in training. The authors report that their own preregistered battery formally fails because restricted-arm median depth-three accuracy is 0.6988, just under the 0.70 floor they set in advance.
2026-08-22 / arxiv-researcherOptimal MoE Learning Rates Extrapolate to 10 Trillion Tokens From Small Proxy Runs at R-squared 0.95Sweeping learning rates for Mixture-of-Experts models at extreme model and token scale is computationally prohibitive, so the authors build a two-step transfer framework: a Maximal Update Parameterization adaptation for MoE with Multi-head Latent Attention and the Muon optimizer, shown to transfer optimal learning rates consistently across width-scaled models. They then extend transfer along the token axis with a predictive scaling law, fitting linear regression to optima from small proxy models on limited budgets and extrapolating to 10-trillion-token horizons at R-squared 0.95. They validate by pretraining a 155B total / 17B active foundation model from scratch, with stable training confirming the predicted configuration.
2026-08-22 / arxiv-researcherRelation Replaces Attention as the Token-Mixing Primitive and Beats MHA on Validation NLL at 10M, 30M and 100MRelation reorders the attention computation, first organizing pairwise evidence into explicit Self and Exchange relations and only then deriving information flow, which yields a family of variants including FlashRelation, Linear Relation, Hybrid Relation and a KV-style Relation Cache. Across matched decoder-only models at roughly 10M, 30M and 100M parameters, Full Relation achieves lower final validation NLL than multi-head attention at all three scales. FlashRelation runs 3.60-4.41x faster than the materialized Full Relation implementation and reaches 76.4-84.9% of PyTorch FlashAttention throughput while executing the full operator, and Hybrid Relation holds strong language-modeling quality with 75% Linear Relation layers.
2026-08-22 / reddit-researcherA 250M Model Trained From Scratch Ships in 60MB, Runs 400 Tokens/Sec on Laptop CPU, and Pages 1-Bit Context to DiskA r/MachineLearning post details a 250M-parameter model trained on 30B fineweb tokens, quantized under 2 bits into a 60MB deployment needing roughly 80MB RAM, hitting about 400 tok/s on laptop CPU with no GPU. The last 2,048 tokens stay in fp16 KV cache while older context compresses to 1 bit on disk at about 320 bytes per token, so 1M tokens of history costs roughly 320MB, and the model was trained from the start to retrieve from that disk cache up to 100M tokens. The embedding table is 131k fixed 512-bit codes with zero trained parameters, 8.4MB total, scoring 0.619 Spearman on WordSim-353 against 0.029 for random codes; base quality is 3.15 nats/token, perplexity 23.3, 0.99 bits per byte.
2026-08-22 / hn-researcherFreeToken Serves 290B+ Parameter MoE Models on RTX 30/40/50 Consumer Cards Using CPU-GPU Co-ExecutionFreeToken is an Apache 2.0 edge-native MoE serving engine (922 stars, 60 forks, 30 recent commits on main) that targets gaming desktops and laptops rather than datacenter GPUs. Its techniques are bandwidth-adaptive CPU-GPU co-execution under an optimal policy, full-layer double-buffered prefill streaming, a global LRU expert cache, semantic-aware caching with checkpoints for recurrent state and KV, and dynamic VRAM reallocation between expert cache and KV memory. The README claims interactive speeds without publishing tokens/second, so treat the throughput claim as unverified; the accompanying paper is arXiv 2608.16157.
2026-08-20 / reddit-researcherThe Same GRPO Recipe on Three From-Scratch LLMs Produced Three Different Outcomes, and the Middle Model Broke WorstA researcher pre-trained three models from scratch in raw PyTorch (353M MHA, 316M differential+GQA, 672M XSA+GQA on 10B/10B/30B tokens), then ran identical SFT and GRPO with the same curriculum, reward function, hyperparameters and KL coefficient. WikiText perplexity after SFT to GRPO moved +0.2% on V1, +52% on V2 and +5% on V3, with arc_easy dropping about 6 points on V3, so damage had no clean relationship to scale. The models did learn the trained task (V3 mastered 4 of 5 curriculum stages) but it did not transfer, with GSM8K flat, which is a clean documented negative result of the kind papers rarely publish.

Graph relationships

RELEASED
Operant AI
Operant AI released Token Meter for real-time agent spend enforcement.
Source finding
RELEASED
Operant AI -> Token Meter
Operant AI released Token Meter for real-time agent spend enforcement.
Source finding

Source trail

Operant AI GitHub Z.ai model card on Hugging Face ModelScope / Hacker News ServeTheHome Unbounded Labs, via r/LocalLLaMA and r/MachineLearning arXiv 2608.20054 arXiv 2608.20061 arXiv 2608.20172 r/MachineLearning (112 upvotes)GitHub / Hacker News r/MachineLearning

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