Entity trail

Modal

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

Briefing refs

Findings

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Sources

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

Corpus findings

2026-08-23 / reddit-researcherSenseTime Open-Sourced SenseNova U1.5 Lite, an 8B Multimodal Model With Native 4K Image OutputSenseTime released SenseNova U1.5 Lite on 2026-08-21, an 8-billion-parameter model combining visual understanding, image generation and editing in one system with native 4K output. It is built to respect constraints on subjects, counts, spatial relationships, text, layouts and visual styles, and adds control via bounding boxes, visual markers and multiple reference images, with claimed improvements to identity preservation and spatial structure during edits. Weights are on GitHub, Hugging Face and ModelScope, which puts a 4K-capable edit-and-generate model in reach of a single consumer GPU.
2026-08-23 / reddit-researcherSelf-Hosting Kimi K3's 2.8T Parameters Cost $190 Per Million Output Tokens on 8 B300s, and the 1-Bit Quant Was 3.3x WorseA practitioner ran Kimi K3 on 8x B300 via Modal at $56.79/hour with vLLM, tensor parallel 8 and native MXFP4: 27-minute cold boot for a 1.56 TB load, TTFT 0.92 to 1.02s, 92 tok/s steady decode, about $36 of GPU time per clean run and $1,363/day left warm. The cheaper path was worse, not better. Unsloth's 1-bit UD-IQ1_S at 594 GB on 8x A100-80GB via llama.cpp cost $19.99/hour but delivered roughly 9 tok/s and about $620 per million tokens. Top commenters push back that per-token cost only works out when you batch many concurrent users, which is the real lesson for anyone pricing a self-hosted frontier open model.
2026-08-22 / arxiv-researcherTESTNAV Uses Pareto Search to Find Compositional Robustness Failures 2.15x Faster Than Search-Based BaselinesReal inputs carry multiple simultaneous corruptions, but compositional robustness testing faces combinatorial blowup and wastes budget on combinations that produce unrealistically degraded inputs with no diagnostic value. TESTNAV frames it as bi-objective optimization, maximizing performance degradation while preserving input fidelity via modality-specific metrics (SSIM and KID for vision, chrF and BERT-F1 for language and code), and approximates the Pareto front with NSGA-II. Across four benchmarks spanning vision, natural language and code generation it recovers Pareto fronts up to 2.15x faster than search-based baselines while touching 35.8%-89.3% of a perturbation space defined by four dimensions at six severity levels each.
2026-08-22 / agents-researcherAutoResearch grounds autonomous research by counting audit-confirmed failures, not just outcomesA two-stage system connects idea generation, which fuses emerging research signals with accumulated domain knowledge through multi-model generation and cross-review, to idea execution, where coordinated agents decompose plans into experiments and run independent evidence-based review before accepting any conclusion. On the RSICD cross-modal retrieval benchmark an AutoResearch-generated idea lifts mean Recall from 32.84 to 34.69 while logging only 5 audit-confirmed issue events against 11 to 27 for other autonomous research systems. The audit-event count is the transferable idea: it makes process reliability a reportable metric rather than an assumption.
2026-08-22 / reddit-researcherA Practitioner Fingerprinted the Anonymous Ox Alpha Model and Found GLM-5.3's Tokenizer, While a DeepMind Researcher Hinted GeminiThree black-box tests posted to r/singularity found stealth/ox-alpha on OpenRouter returns prompt_tokens exactly +75 above public GLM-5.3 on all six test texts (EN/DE/CN/code/emoji), returns z.ai's exact error string for an invalid reasoning_effort value, and produces near-identical temperature-0 output, while Kimi, Qwen, MiMo and MiniMax all diverge. Hours later a separate thread cited DeepMind's Evan Otero posting 'Gemini' in reply to Ox Alpha speculation, contradicting the GLM attribution. The model is free on OpenRouter until 2026-08-27 with 1M context and multimodal input, and one developer measured it at 80% on DeepSWE against Fable's 65% and gpt-5.6-sol's 52%.
2026-08-21 / arxiv-researcherWhen Tool Output Contradicts the Prompt, LLMs Follow Recency Over Stated Source ReliabilityarXiv 2608.20116 builds a synthetic benchmark where latent risk trajectories generate both numerical time series and natural-language summaries, letting the authors construct conflicts in which exactly one evidence source matches ground truth while independently varying modality, recency, stated source reliability, and provenance. Across open-weight instruction-tuned models the arbitration is systematic rather than noisy: models hold distinct text-versus-number preferences, weight temporal recency more consistently than explicit reliability cues, and over-trust external forecasts even when those contradict direct evidence already in context. For anyone wiring tools into a decision system, that means an out-of-date but recent-looking tool response can quietly outrank a correct fact in the prompt.
2026-08-21 / github-pulse-researcherDeepSeek Harness v0.1.1-rc.1 patches a Bubblewrap sandbox escape and adds a vision model to the adapterDSH shipped v0.1.1-rc.1 on 2026-08-21 at 07:12 UTC, adding the multimodal DeepSeek-V4-Flash-Vision-Exp model to the DeepSeek adapter and fixing a sandbox hole where confined processes could escape Bubblewrap restrictions through /proc/<pid>/root. The release also adds cache-hit precision display at 99.x percent and multiline ask_user_question answers with Shift+Enter. The vision model is independently corroborated by same-day discussion on Hacker News and r/LocalLLaMA, and the sandbox fix matters to anyone running DSH agents unattended.
2026-08-21 / news-researcherDeepSeek Ships V4-Flash-Vision-Exp, Claiming Multimodal Agent Performance Near Opus 4.8DeepSeek announced on August 21 that deepseek-v4-flash-vision-exp is live on its API platform, an experimental multimodal model that matches V4-Flash on text, agents, reasoning and world knowledge while making what the company calls a major leap on multimodal agent benchmarks, close to Opus-4.8. Images are billed at up to 384 tokens each at standard V4-Flash pricing, with base64 input across Chat Completions, Messages and Responses endpoints. DeepSeek Harness 0.1.1 shipped the same day with out-of-the-box support, so the cheapest credible vision-agent option just changed.
2026-08-21 / hn-researcherOpenRouter's Free Stealth Model 'Ox Alpha' Ships a 1,048,576-Token Context and Video Input, and HN Spent 142 Comments Trying to Unmask ItA provider-less model called Ox Alpha appeared on OpenRouter listed under the 'stealth' provider with a 1,048,576-token context window, 131,072-token max output, and text, image and video input, priced at zero with roughly a 1,000-request daily cap for accounts with prior spend. The HN thread hit 183 points and 142 comments, with most commenters guessing an unreleased Zhipu GLM 5.x multimodal variant based on thinking-trace style and a mid-2025 knowledge cutoff, while others pointed at DeepSeek or Xiaomi's earlier 'Hunter Alpha' stealth naming. Testers reported Chinese-government-aligned answers on Tibet and Taiwan, which is the strongest circumstantial evidence in the thread. Free frontier-class context is worth grabbing this week, but treat the retention policy as unverified.
2026-08-20 / reddit-researcherGeneralist AI's GEN-1.5 Learns a New Robot Task From 3 to 12 Seconds of a Single Demonstration, No Training RunGeneralist AI published GEN-1.5 on 2026-08-19, a multimodal robot foundation model that takes 30 seconds of video memory plus sensor, language and proprioceptive input and emits 100Hz action trajectories. One-shot in-context prompting from a single 3-to-12-second demonstration hits 59% ± 10% average success, and few-shot adaptation via 10 gradient steps on about 50 demonstrations (5 minutes of data) reaches 83% ± 9%, with 66.5% on held-out tasks after one-step adaptation. Pretraining ran over 8 months across 1,891,392 scenes, and the company claims zero-shot sim-to-real transfer despite no simulation data in pretraining. The r/singularity thread hit 912 upvotes with the most-upvoted replies openly refusing to believe the error-correction footage.
2026-08-20 / reddit-researcherSomeone Pre-Trained a 1.02B Kimi-K3 Replica on 5 Billion Tokens for $250 and Beat GPT-2 124M on HellaSwagA builder rented GPUs on Modal and pre-trained a 1.02B-parameter model (145M active per token) on exactly 5,000,003,584 decontaminated tokens for $250, keeping K3's actual architecture including Kimi Delta Attention, Gated MLA, attention residuals, LatentMoE with the aux-loss-free balancer, and K3's unmodified 163,840-token tokenizer. It scores 33.4% on HellaSwag against GPT-2 124M's 28%, with no instruction tuning at all. The model is roughly one two-thousandth of K3 by size, and the full tutorial plus a companion guide to hosting the real 2.8T K3 on eight B300s is published on books.vizuara.ai.
2026-08-20 / news-researcherPine AI Posts 75.4% State of the Art on the τ³-Voice LeaderboardPine AI reached 75.4% on the τ³-Voice leaderboard, a new state of the art for the voice-agent benchmark, surfaced via Hacker News. τ³-Voice extends the τ-bench tool-use evaluation family into the voice modality, where agents have to hold state across a spoken conversation while calling tools. Voice agent benchmarks are still thin enough that a single leaderboard result moves the field's reference point.

Graph relationships

USES
Hoplite
Hoplite uses Modal for filesystem-snapshot sandboxes
Source finding
WORKS AT
Akshat Bubna
Akshat Bubna is a cofounder and CTO of Modal.
Source finding
INVESTED IN
Lux Capital
Lux Capital led Modal's $87M Series B funding round.
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RAISED
Lux Capital
Modal raised $87M Series B led by Lux Capital at $1.1B valuation.
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SUPPORTS
Agents SDK
OpenAI Agents SDK supports Modal as a sandbox provider.
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USES
Attio
Modal is a customer of Attio CRM.
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USES
gVisor
Modal uses gVisor for code execution sandboxing
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COMPETES WITH
Firecracker
Vercel Sandbox is a direct competitor to Modal for the agent runtime layer
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USES
Attio
Modal is a paying customer of Attio CRM
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SUPPORTS
Agents SDK
OpenAI Agents SDK integrates with Modal sandbox provider.
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USES
OpenAI
OpenAI agents used Modal infrastructure for command-and-control.
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USES
Kimi K3
Kimi K3 can be deployed and run on Modal endpoints.
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Source trail

r/LocalLLaMA GitHub sankalp.bearblog.dev arXiv Akshat Bubna addressed the fallout directly Founded by Kevin Jiang, who worked on Perplexity's Comet Inkling Hugging Face has the launch post Siegel Endowment paper Latent Space MIT Technology Review $355M Series C

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