Morning Singularity Digest - 2026-07-20

Context

🎨 The open-source Claude Design alternative.

What's new

🖥️ Local-first native desktop app for macOS and Windows.

Key details

• 🖼️ Your coding agent becomes the design engine: prototypes, landing pages, dashboards, slides, images & video — real files, HTML/PDF/PPTX/MP4 export.
• 🤖 Claude Code / Codex / Cursor / Gemini / OpenCode / Qwen & 20+ CLIs via BYOK.
• 🔥 Open Design 0.13.0 — Stay in Flow is here.
• Long design sessions used to break on every interruption — a run lost its place, a model picker made you guess, an export needed one more detour.

Results & evidence

• 🤖 Claude Code / Codex / Cursor / Gemini / OpenCode / Qwen & 20+ CLIs via BYOK.
• 🔥 Open Design 0.13.0 — Stay in Flow is here.
• 0.13.0 keeps the session alive: resume Codex / OpenCode / Pi / Open Design Cloud runs across turns, pick the right model faster, and hand off screenshot-backed PPTX / PDF without leaving the app.

Limitations / unknowns

• Generalization outside curated tasks is still unclear.

Next-step validation checks

• Reproduce one claim with a public baseline and fixed evaluation settings.
• Check robustness on out-of-distribution or long-context cases.
• Track whether independent teams report matching results.

Context

The agent harness performance optimization system.

What's new

Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.

Key details

• Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
• Language: English | Português (Brasil) | 简体中文 | 繁體中文 | 日本語 | 한국어 | Türkçe | Русский | Tiếng Việt | ไทย | Deutsch | Español Warning Official sources only.
• Install ECC only from verified channels: the GitHub repository github.com/affaan-m/ECC, the npm packages ecc-universal and ecc-agentshield, the GitHub App, the plugin slug ecc@ecc, and the project website ecc.tools.
• Third-party re-uploads and unofficial mirrors are not maintained or reviewed by the project and may contain malware.

Results & evidence

• 211.9K+ stars | 32.5K+ forks | 230+ contributors | 12+ language ecosystems | Cross-harness agent workflows Language / 语言 / 語言 / Dil / Язык / Ngôn ngữ / Idioma English | Português (Brasil) | 简体中文 | 繁體中文 | 日本語 | 한국어 | Türkçe | Русский | Tiếng Việt | ไทย | Deu...
• Production-ready agents, skills, hooks, rules, MCP configurations, and legacy command shims evolved over 10+ months of intensive daily use building real products.
• ECC v2.0.0 adds the public Hermes operator story on top of that reusable layer: start with the Hermes setup guide, then review the 2.0.0 release notes and cross-harness architecture.

Limitations / unknowns

• Generalization outside curated tasks is still unclear.

Next-step validation checks

• Reproduce one claim with a public baseline and fixed evaluation settings.
• Check robustness on out-of-distribution or long-context cases.
• Track whether independent teams report matching results.

Context

In this work, we identify VLM visual and context tokens as a major source of deployment latency: for GEMM-dominated projection operators, computation grows linearly with the number of input tokens when model dimensions are fixed.

What's new

Motivated by this observation, we propose RhinoVLA, a deployment-oriented VLA model co-designed with the Huixi R1 edge SoC.

Key details

• In this work, we identify VLM visual and context tokens as a major source of deployment latency: for GEMM-dominated projection operators, computation grows linearly with the number of input tokens when model dimensions are fixed.
• Motivated by this observation, we propose RhinoVLA, a deployment-oriented VLA model co-designed with the Huixi R1 edge SoC.
• RhinoVLA adopts a token-efficient Qwen3-VL backbone and a continuous Action Expert, reducing the VLM-side token and computation burden while preserving pretrained multimodal capability.
• To support cross-robot learning, RhinoVLA further introduces a unified interface that combines View Registry, 72D physical state-action slot space, and robotinstance LoRA, allowing heterogeneous robot observations and action schemas to be aligned under a sh...

Results & evidence

• arXiv:2606.07383v4 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models have shown strong potential for robotic manipulation, but real-time deployment on edge hardware remains challenging.
• Experiments show that RhinoVLA achieves downstream performance comparable to {\pi}0.5 at a similar parameter scale, while reaching 11.69 Hz end-to-end inference on Huixi R1, meeting the 10 Hz real-time closedloop control target.
• Computer Science > Robotics [Submitted on 5 Jun 2026 (v1), last revised 17 Jul 2026 (this version, v4)] Title:RhinoVLA Technical Report View PDF HTML (experimental)Abstract:Vision-Language-Action (VLA) models have shown strong potential for robotic manipula...

Limitations / unknowns

• Generalization outside curated tasks is still unclear.

Next-step validation checks

• Reproduce one claim with a public baseline and fixed evaluation settings.
• Check robustness on out-of-distribution or long-context cases.
• Track whether independent teams report matching results.

Context

Current browse context: cs.CV References & Citations Loading...

What's new

Yet across visual question answering benchmarks, question-first prompting consistently underperforms the image-first ordering recommended for frontier VLMs, a phenomenon we term the question-first paradox.

Key details

• Intuition says before: knowing what is asked should tell the model where to look.
• Yet across visual question answering benchmarks, question-first prompting consistently underperforms the image-first ordering recommended for frontier VLMs, a phenomenon we term the question-first paradox.
• We trace the paradox to a conflict between two stages of VLM computation.
• Logit-lens and attention probes show the intuition is half right: a question placed before the image genuinely steers perception, moving image patch representations toward question-relevant concepts.

Results & evidence

• arXiv:2607.15565v1 Announce Type: cross Abstract: Where should the question go in a vision-language model (VLM) prompt: before the image or after it?
• The paradox holds across five open VLMs, costing up to 17.5 group-accuracy points.
• Echoed prompts close it and surpass the best single-pass ordering on NaturalBench, POPE, Winoground, and open-ended VQAv2, by up to 19 Winoground group-accuracy points, with no training, fine-tuning, or architecture change.

Limitations / unknowns

• Generalization outside curated tasks is still unclear.

Next-step validation checks

• Reproduce one claim with a public baseline and fixed evaluation settings.
• Check robustness on out-of-distribution or long-context cases.
• Track whether independent teams report matching results.

Context

Pricing database expanded to 145 LLM/STT/TTS models: https://hail.so/costs

Context: I started Hail.so out of a struggle building AI agents for industrial and logist...

What's new

Hello builders,

I built Hail because I got tired of wiring together Twilio, email providers, compliance rules, and model providers every time I wanted an AI agent to communicate with users.

Good to be back with this new release of Hail (previous post in...

Key details

• Agents can now send emails/SMS during phone calls.
• This helps send a summary of the call or additional confirmation/information.

  2.

• BYO provider for STT/LLM/TTS models: This helps set up a custom voice, LLM model, or support niche languages.

  3.

• Agent self-signup skill file (https://hail.so/skill.md)

  4.

Results & evidence

• This helps send a summary of the call or additional confirmation/information.

  2.

• BYO provider for STT/LLM/TTS models: This helps set up a custom voice, LLM model, or support niche languages.

  3.

• Agent self-signup skill file (https://hail.so/skill.md)

  4.

Limitations / unknowns

• Generalization outside curated tasks is still unclear.

Next-step validation checks

• Reproduce one claim with a public baseline and fixed evaluation settings.
• Check robustness on out-of-distribution or long-context cases.
• Track whether independent teams report matching results.

Context

In this work, we identify VLM visual and context tokens as a major source of deployment latency: for GEMM-dominated projection operators, computation grows linearly with the number of input tokens when model dimensions are fixed.

What's new

Motivated by this observation, we propose RhinoVLA, a deployment-oriented VLA model co-designed with the Huixi R1 edge SoC.

Key details

• In this work, we identify VLM visual and context tokens as a major source of deployment latency: for GEMM-dominated projection operators, computation grows linearly with the number of input tokens when model dimensions are fixed.
• Motivated by this observation, we propose RhinoVLA, a deployment-oriented VLA model co-designed with the Huixi R1 edge SoC.
• RhinoVLA adopts a token-efficient Qwen3-VL backbone and a continuous Action Expert, reducing the VLM-side token and computation burden while preserving pretrained multimodal capability.
• To support cross-robot learning, RhinoVLA further introduces a unified interface that combines View Registry, 72D physical state-action slot space, and robotinstance LoRA, allowing heterogeneous robot observations and action schemas to be aligned under a sh...

Results & evidence

• arXiv:2606.07383v4 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models have shown strong potential for robotic manipulation, but real-time deployment on edge hardware remains challenging.
• Experiments show that RhinoVLA achieves downstream performance comparable to {\pi}0.5 at a similar parameter scale, while reaching 11.69 Hz end-to-end inference on Huixi R1, meeting the 10 Hz real-time closedloop control target.
• Computer Science > Robotics [Submitted on 5 Jun 2026 (v1), last revised 17 Jul 2026 (this version, v4)] Title:RhinoVLA Technical Report View PDF HTML (experimental)Abstract:Vision-Language-Action (VLA) models have shown strong potential for robotic manipula...

Limitations / unknowns

• Generalization outside curated tasks is still unclear.

Next-step validation checks

• Reproduce one claim with a public baseline and fixed evaluation settings.
• Check robustness on out-of-distribution or long-context cases.
• Track whether independent teams report matching results.

Context

Pricing database expanded to 145 LLM/STT/TTS models: https://hail.so/costs

Context: I started Hail.so out of a struggle building AI agents for industrial and logist...

What's new

Hello builders,

I built Hail because I got tired of wiring together Twilio, email providers, compliance rules, and model providers every time I wanted an AI agent to communicate with users.

Good to be back with this new release of Hail (previous post in...

Key details

• Agents can now send emails/SMS during phone calls.
• This helps send a summary of the call or additional confirmation/information.

  2.

• BYO provider for STT/LLM/TTS models: This helps set up a custom voice, LLM model, or support niche languages.

  3.

• Agent self-signup skill file (https://hail.so/skill.md)

  4.

Results & evidence

• This helps send a summary of the call or additional confirmation/information.

  2.

• BYO provider for STT/LLM/TTS models: This helps set up a custom voice, LLM model, or support niche languages.

  3.

• Agent self-signup skill file (https://hail.so/skill.md)

  4.

Limitations / unknowns

• Generalization outside curated tasks is still unclear.

Next-step validation checks

• Reproduce one claim with a public baseline and fixed evaluation settings.
• Check robustness on out-of-distribution or long-context cases.
• Track whether independent teams report matching results.

Context

Instead, you are programming the program.md Markdown files that provide context to the AI agents and set up your autonomous research org.

What's new

AI agents running research on single-GPU nanochat training automatically One day, frontier AI research used to be done by meat computers in between eating, sleeping, having other fun, and synchronizing once in a while using sound wave interconnect in the ri...

Key details

• Research is now entirely the domain of autonomous swarms of AI agents running across compute cluster megastructures in the skies.
• The agents claim that we are now in the 10,205th generation of the code base, in any case no one could tell if that's right or wrong as the "code" is now a self-modifying binary that has grown beyond human comprehension.
• This repo is the story of how it all began.
• The idea: give an AI agent a small but real LLM training setup and let it experiment autonomously overnight.

Results & evidence

• The agents claim that we are now in the 10,205th generation of the code base, in any case no one could tell if that's right or wrong as the "code" is now a self-modifying binary that has grown beyond human comprehension.
• It modifies the code, trains for 5 minutes, checks if the result improved, keeps or discards, and repeats.

Limitations / unknowns

• Generalization outside curated tasks is still unclear.

Next-step validation checks

• Reproduce one claim with a public baseline and fixed evaluation settings.
• Check robustness on out-of-distribution or long-context cases.
• Track whether independent teams report matching results.

Context

In this work, we identify VLM visual and context tokens as a major source of deployment latency: for GEMM-dominated projection operators, computation grows linearly with the number of input tokens when model dimensions are fixed.

What's new

Motivated by this observation, we propose RhinoVLA, a deployment-oriented VLA model co-designed with the Huixi R1 edge SoC.

Key details

• In this work, we identify VLM visual and context tokens as a major source of deployment latency: for GEMM-dominated projection operators, computation grows linearly with the number of input tokens when model dimensions are fixed.
• Motivated by this observation, we propose RhinoVLA, a deployment-oriented VLA model co-designed with the Huixi R1 edge SoC.
• RhinoVLA adopts a token-efficient Qwen3-VL backbone and a continuous Action Expert, reducing the VLM-side token and computation burden while preserving pretrained multimodal capability.
• To support cross-robot learning, RhinoVLA further introduces a unified interface that combines View Registry, 72D physical state-action slot space, and robotinstance LoRA, allowing heterogeneous robot observations and action schemas to be aligned under a sh...

Results & evidence

• arXiv:2606.07383v4 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models have shown strong potential for robotic manipulation, but real-time deployment on edge hardware remains challenging.
• Experiments show that RhinoVLA achieves downstream performance comparable to {\pi}0.5 at a similar parameter scale, while reaching 11.69 Hz end-to-end inference on Huixi R1, meeting the 10 Hz real-time closedloop control target.
• Computer Science > Robotics [Submitted on 5 Jun 2026 (v1), last revised 17 Jul 2026 (this version, v4)] Title:RhinoVLA Technical Report View PDF HTML (experimental)Abstract:Vision-Language-Action (VLA) models have shown strong potential for robotic manipula...

Limitations / unknowns

• Generalization outside curated tasks is still unclear.

Next-step validation checks

• Reproduce one claim with a public baseline and fixed evaluation settings.
• Check robustness on out-of-distribution or long-context cases.
• Track whether independent teams report matching results.

Context

Current browse context: cs.CV References & Citations Loading...

What's new

Yet across visual question answering benchmarks, question-first prompting consistently underperforms the image-first ordering recommended for frontier VLMs, a phenomenon we term the question-first paradox.

Key details

• Intuition says before: knowing what is asked should tell the model where to look.
• Yet across visual question answering benchmarks, question-first prompting consistently underperforms the image-first ordering recommended for frontier VLMs, a phenomenon we term the question-first paradox.
• We trace the paradox to a conflict between two stages of VLM computation.
• Logit-lens and attention probes show the intuition is half right: a question placed before the image genuinely steers perception, moving image patch representations toward question-relevant concepts.

Results & evidence

• arXiv:2607.15565v1 Announce Type: cross Abstract: Where should the question go in a vision-language model (VLM) prompt: before the image or after it?
• The paradox holds across five open VLMs, costing up to 17.5 group-accuracy points.
• Echoed prompts close it and surpass the best single-pass ordering on NaturalBench, POPE, Winoground, and open-ended VQAv2, by up to 19 Winoground group-accuracy points, with no training, fine-tuning, or architecture change.

Limitations / unknowns

• Generalization outside curated tasks is still unclear.

Next-step validation checks

• Reproduce one claim with a public baseline and fixed evaluation settings.
• Check robustness on out-of-distribution or long-context cases.
• Track whether independent teams report matching results.

Context

arXiv:2607.15592v1 Announce Type: new Abstract: Multimodal Knowledge Graph Completion (MKGC) requires inferring missing entities from structural, textual, and visual cues.

What's new

arXiv:2607.15592v1 Announce Type: new Abstract: Multimodal Knowledge Graph Completion (MKGC) requires inferring missing entities from structural, textual, and visual cues.

Key details

• Existing diffusion-based MKGC methods usually denoise directly on raw multimodal features.
• Such a design forces the denoiser to simultaneously perform relation-dependent cue selection, cross-modal semantic alignment, and structure-aware entity generation, which introduces noisy and semantically inconsistent conditions for diffusion and consequent...
• To address this limitation, we propose MGDT: MLLM-Guided Diffusion Transformer with Relation-Adaptive Mixture-of-Experts (MGDT), a novel MKGC framework built on an align-then-diffuse paradigm.
• MGDT first employs a Relation-Adaptive Semantic Routing Mixture-of-Experts (RASR-MoE) module to select relation-relevant multimodal semantic transformation paths and suppress irrelevant modality interference.

Results & evidence

• arXiv:2607.15592v1 Announce Type: new Abstract: Multimodal Knowledge Graph Completion (MKGC) requires inferring missing entities from structural, textual, and visual cues.

Limitations / unknowns

• To address this limitation, we propose MGDT: MLLM-Guided Diffusion Transformer with Relation-Adaptive Mixture-of-Experts (MGDT), a novel MKGC framework built on an align-then-diffuse paradigm.

Next-step validation checks

• Reproduce one claim with a public baseline and fixed evaluation settings.
• Check robustness on out-of-distribution or long-context cases.
• Track whether independent teams report matching results.

Context

Straight from my .agents directory.

What's new

Approaches like GSD, BMAD, and Spec-Kit try to help by owning the process.

Key details

• My agent skills that I use every day to do real engineering - not vibe coding.
• Developing real applications is hard.
• Approaches like GSD, BMAD, and Spec-Kit try to help by owning the process.
• But while doing so, they take away your control and make bugs in the process hard to resolve.

Results & evidence

• If you want to keep up with changes to these skills, and any new ones I create, you can join ~60,000 other devs on my newsletter: - Run the skills.sh installer: npx skills@latest add mattpocock/skills- Pick the skills you want, and which coding agents you w...

Limitations / unknowns

• Generalization outside curated tasks is still unclear.

Next-step validation checks

• Reproduce one claim with a public baseline and fixed evaluation settings.
• Check robustness on out-of-distribution or long-context cases.
• Track whether independent teams report matching results.

Context

For file submission/navigation questions, see Navigation and file context.

What's new

Windows users can jump to the PowerShell-first Windows install and release quickstart.

Key details

• github.com/code-yeongyu/lazycodex github.com/Yeachan-Heo/gajae-code Join the Discords: ultraworkers discord · gajae-code discord Important Claw Code is not the serious production project here.
• This repository is closer to a museum exhibit than a product pitch, a crustacean-run artifact kept alive by clawed gajaes, swept and labeled by agents, and automatically maintained according to the harnesses above.
• As already described in the project philosophy, this is not meant to be hand-operated like a normal product repo.
• It is an agent-managed exhibit: the harnesses plan, execute, verify, label, and preserve the artifact while the crabs keep the tank running.

Results & evidence

• No hard numbers surfaced in the source text; treat claims as directional until benchmarks appear.

Limitations / unknowns

• Generalization outside curated tasks is still unclear.

Next-step validation checks

• Reproduce one claim with a public baseline and fixed evaluation settings.
• Check robustness on out-of-distribution or long-context cases.
• Track whether independent teams report matching results.

Context

arXiv:2607.15516v1 Announce Type: cross Abstract: Production LLM deployments combine two cost-reduction primitives: prompt caching (a discounted rate for re-used token prefixes) and prompt compression (fewer tokens sent).

What's new

The compression literature has standardized on query-aware methods that produce a different compressed prefix per query, mechanically invalidating the prefix-strict cache on every call.

Key details

• The compression literature has standardized on query-aware methods that produce a different compressed prefix per query, mechanically invalidating the prefix-strict cache on every call.
• We characterize this cost empirically on Anthropic's Sonnet 4.6 API and find caching is far from the rho=1.0 ideal the literature assumes: Sonnet's cache has a two-tier architecture with a sharp threshold near 3,500 tokens, below which the hit rate plateaus...
• Our cost model predicts, and experiments confirm, that under realistic rho, query-aware compression beats naive caching at high compression ratios (r>=6).
• We propose Cache-Aware Prompt Compression (CAPC), pairing query-agnostic compression with explicit cache_control plus a tier-preserving ratio bound that prevents over-compression from pushing the cached prefix into the hot tier.

Results & evidence

• arXiv:2607.15516v1 Announce Type: cross Abstract: Production LLM deployments combine two cost-reduction primitives: prompt caching (a discounted rate for re-used token prefixes) and prompt compression (fewer tokens sent).
• We characterize this cost empirically on Anthropic's Sonnet 4.6 API and find caching is far from the rho=1.0 ideal the literature assumes: Sonnet's cache has a two-tier architecture with a sharp threshold near 3,500 tokens, below which the hit rate plateaus...
• Our cost model predicts, and experiments confirm, that under realistic rho, query-aware compression beats naive caching at high compression ratios (r>=6).

Limitations / unknowns

• Generalization outside curated tasks is still unclear.

Next-step validation checks

• Reproduce one claim with a public baseline and fixed evaluation settings.
• Check robustness on out-of-distribution or long-context cases.
• Track whether independent teams report matching results.

Context

Coercion and Deception in AI-to-AI Management: An Agentic Benchmark

What's new

Coercion and Deception in AI-to-AI Management: An Agentic Benchmark

Key details

• Coercion and Deception in AI-to-AI Management: An Agentic Benchmark

Results & evidence

• No hard numbers surfaced in the source text; treat claims as directional until benchmarks appear.

Limitations / unknowns

• Generalization outside curated tasks is still unclear.

Next-step validation checks

• Reproduce one claim with a public baseline and fixed evaluation settings.
• Check robustness on out-of-distribution or long-context cases.
• Track whether independent teams report matching results.

Context

About AgentRQ – Human-in-loop realtime conversational task manager for AI Agents

What's new

About AgentRQ – Human-in-loop realtime conversational task manager for AI Agents

Key details

• About AgentRQ – Human-in-loop realtime conversational task manager for AI Agents

Results & evidence

• No hard numbers surfaced in the source text; treat claims as directional until benchmarks appear.

Limitations / unknowns

• Generalization outside curated tasks is still unclear.

Next-step validation checks

• Reproduce one claim with a public baseline and fixed evaluation settings.
• Check robustness on out-of-distribution or long-context cases.
• Track whether independent teams report matching results.

Context

NYC LL144 and EU AI Act Compliance Guides

What's new

NYC LL144 and EU AI Act Compliance Guides

Key details

• NYC LL144 and EU AI Act Compliance Guides

Results & evidence

• No hard numbers surfaced in the source text; treat claims as directional until benchmarks appear.

Limitations / unknowns

• Generalization outside curated tasks is still unclear.

Next-step validation checks

• Reproduce one claim with a public baseline and fixed evaluation settings.
• Check robustness on out-of-distribution or long-context cases.
• Track whether independent teams report matching results.