Entity trail

Modal

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

Briefing refs

Findings

Edges

Sources

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

Corpus findings

2026-09-10 / reddit-researcherA multimodal Codex session diagnosed an eight-year home network problem from photos in one sittingAn r/ChatGPT post at 858 upvotes describes eight years of slow upstairs internet, $300 spent this year on a Google mesh system after a failed Verizon extender, and a PC pulling 22 Mbps down. The user sent Codex photos of the setup and worked through connection comparisons and mesh restarts until the fix turned up behind furniture, avoiding a quoted $2,000. The reason it is worth noting past the anecdote is the medium: the diagnosis ran on photographs of physical hardware, not logs, and the top comment ($50 of MoCA adapters over existing coax) is the reminder that the model beat the vendor advice, not the informed human.
2026-09-10 / sources-researcherDeepSeek released V4.1-Flash: a 552B causal encoder-decoder that activates 8B params on prefill and 16B on decodeDeepSeek pushed V4.1-Flash to Hugging Face at 02:17 UTC on 2026-09-10, an MIT-licensed multimodal MoE with 552B backbone params and a 1M-token context. The Causal Encoder-Decoder architecture projects the decoder's global KV cache from final encoder hidden states rather than per-layer, so only 8B params activate during prefill and 16B during decode; SWA Bounded Replay cuts the persistent KV cache footprint to roughly 1/8 of V4-Flash. It posts Terminal-Bench 2.1 at 90.6, DeepSWE v1.1 at 74.2 and Codeforces 3471, and the card includes a scaffold comparison showing its own minimal harness (90.6) beating Claude Code (88.0) and Codex (84.1) on the same model.
2026-09-09 / reddit-researcherNex shipped an Apache-2.0 computer-use family: Mini 35B, Pro 397B and a text-only 1.6T MaxThe Nex-N2.5 family went live September 8 with Apache-2.0 weights. Mini is a 35B/~3B-active multimodal MoE with 262K context, 256 routed experts (8 active plus a shared expert) and ~70GB of BF16 safetensors, deployable on two H100s; Pro at 397B scores 56.4 on OSWorld-2 against Qwen3.8-Max at 46.7, plus OSWorld-G 87.4, OSWorld-Verified 82.2 and Vision2Web 68.2. Max at 1.6T is text-only and scores 50.2 on AutomationBench v1.0.6, 0.1 behind Claude Opus 5. The r/LocalLLaMA thread for Mini drew 79 upvotes and 43 comments.
2026-09-09 / reddit-researcherDeepSeek V4.1 Flash is live on the public API for two days only, under a model ID that expires September 10DeepSeek opened an intermediate V4.1 Flash build to all API users on September 8 at about 3pm Beijing time via the model ID deepseek-v4.1-flash-expires-on-0910, with no beta application, the same base_url, V4 Flash pricing and 20 concurrent requests per account. The company describes a new architecture with multimodal support built into the model rather than bolted on as with V4-Flash-Vision-Exp, handling text, image and speech in one pass. Developer measurements put output above 300 tok/s with a peak of 507 tok/s.
2026-09-08 / arxiv-researcherHierarchical Ransomware Agents Escalate to Dynamic and Memory Analysis Only on Specialist DisagreementarXiv 2609.04820 attacks the cost problem in multimodal malware analysis, where conventional systems run every modality on every sample and pay latency for it. Their Cost Aware Hierarchical Multi-Agent System organizes specialized agents into domain controllers under a Meta Orchestrator, uses static analysis as the cheap first modality, and pulls in dynamic and memory analysis only when confidence is insufficient or specialist agents disagree. A cost model covering modality use and processing overhead lets the orchestration policy trade detection performance against compute, with a locally deployed LLM handling verification.
2026-09-08 / arxiv-researcherSciDocBench: The Strongest Multimodal System Scores 62.6 of 100 on Real Scientific Reading WorkflowsarXiv 2609.05141 introduces SciDocBench, 124 expert-authored and difficulty-screened questions grouped into seven research-assistant capability groups and 19 subtasks across five scientific domains, each instantiated under four matched conditions combining English or Chinese questions with all-images-first or interleaved document representations, giving 496 evaluation instances. The strongest evaluated system reached only 62.6/100, with the weakest areas being document perception, evidence grounding, verification and cross-document reasoning. The authors also ship SciDocIR, a typed evidence-graph representation preserving document objects, layout, cross-references and provenance, plus a roughly 15K-example supervised dataset built on it.
2026-09-08 / thought-leaders-researcherJay Alammar and Maarten Grootendorst ship An Illustrated Guide to AI Agents with 300+ original figuresThe pair announced the ebook is now live on Kindle and other stores with print copies shipping in a few weeks, after a year of work and over 300 original diagrams. It covers agent architecture (tools, memory, planning), reasoning and multimodal LLMs, multi-agent collaboration, and deployment-side techniques including distillation, quantization and RL. Alammar's Illustrated Transformer is the reference most engineers actually learned attention from, which is the reason to expect this to become the default shared vocabulary for agent design.
2026-09-07 / sources-researcherCUA-Universe builds hybrid GUI+CLI agent environments automatically, on the argument that GUI-only computer-use agents produce inefficient trajectoriesarXiv 2609.05374 (2026-09-04) makes a case against the OSWorld/AndroidWorld framing: real computer work combines visual state inspection with precise high-throughput command-line operations, but CLI-native agents lack visual perception for layout-dependent tasks while GUI-native agents waste steps on things a command would do in one. The blocker has been that supporting both modalities over real applications takes manual engineering per app, so the contribution is a pipeline: App-Forge adapts applications into reproducible VMs and discovers, wraps or generates command-line surfaces, scaling to 16 applications; Task-Weave synthesizes hybrid tasks of controllable difficulty from reusable operations over seed files; Path-Steer steers rollouts along efficient hybrid paths and harvests verified trajectories for post-training. Training on the resulting data shifts behavior away from brittle CLI scripting and inefficient GUI clicking.
2026-09-06 / arxiv-researcherReplay Selection Alone Matches Full Replay Pipelines and Cuts Agentic-Search Wall-Clock TimeRL post-training is bottlenecked by fresh rollout generation, and in agentic settings environment interaction dominates wall-clock cost, but existing replay methods bury their contribution inside exploration and mixed-policy machinery. Headroom-Drift Replay is a group-level control primitive for GRPO splitting reuse into two decisions: Headroom ranks stored groups by remaining learning value, Drift gates them by compatibility with the current policy, with the fresh on-policy stream untouched and no auxiliary generation added. Across mathematical reasoning, multimodal reasoning and Agentic Search it beats naive replay and matches or exceeds broader replay methods on Avg Mean@32, delivering comparable quality at materially lower wall-clock time where environment calls dominate.
2026-09-05 / arxiv-researcherAlcaTRAz Defends Jailbreaks With Character-Level Perturbation Rules and No Model Access, Beating Llama Guard on 73.4% of CombinationsarXiv 2609.03693 proposes a prompt-level jailbreak defense that needs no weights, internals, retraining or modification of the target model, learning a transferable transformation rule that inserts controlled character-level perturbations at selected positions to disrupt the structural regularities jailbreaks exploit. Across 33 open-weight models and 22 jailbreak attack types, it achieved the best composite security and functionality score in 73.4% of model-attack combinations against Llama Guard, RA-LLM and Goal Prioritization, shifting the aggregate severity score from a modal 10 to a modal 2 while keeping mean benign score within 0.27 points of undefended (8.35 vs 8.62). The authors explicitly decline to claim a guarantee, noting a residual high-severity tail and no evaluation against adaptive attackers.
2026-09-05 / rss-researcherNeoMME gets within 0.002 nDCG of ColQwen2.5 on document retrieval using 14x fewer parametersH Company released NeoMME on September 3, a multimodal-native encoder at 260M and 800M parameters that runs text tokens and raw image patches through one bidirectional Transformer with no separate vision tower or text decoder, using a from-scratch 131k BPE vocabulary, 16,384-token context, 32x32 dynamic-resolution patches, grouped-query attention and 2D RoPE. On ViDoRe v3 the 260M scores 0.523 nDCG@10, best among sub-800M models and within 0.002 of ColQwen2.5 at roughly 14x fewer parameters, while the 800M reaches 0.556. Throughput is 51 pages per second at 2048x2048 on an L40S, about double ColModernVBERT, and all checkpoints are Apache 2.0, which makes it a cheap swap for anyone running document RAG.
2026-09-05 / news-researcherAWS Shipped a Reference Build for a Voice-and-Text Ordering Agent on a Single WhatsApp NumberAWS published a deployable multimodal ordering assistant built on Bedrock AgentCore that takes orders through text, voice notes and real-time voice calls on one business phone number. The interesting engineering is the single-channel multimodality: routing three input modes into one agent session rather than maintaining separate voice and chat stacks. It is one of the few end-to-end agent reference architectures published this week that includes the telephony path.

Graph relationships

USES
Inspect
Inspect uses Modal sandboxes provisioning in under 5 seconds.
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USES
OpenSpender
OpenSpender supports Modal compute.
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USES
Hoplite
Hoplite uses Modal for filesystem-snapshot sandboxes
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WORKS AT
Akshat Bubna
Akshat Bubna is a cofounder and CTO of Modal.
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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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Source trail

The September 2 entry v0.1.117 Quesma instead of standardizing on Claude Code or Cursor arXiv 2608.23953 The end of programming now running reliably on millions of developer machines Posted to Show HN August 26 Kimi K3 on 8x B300 r/LocalLLaMA GitHub sankalp.bearblog.dev

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