# Humble’s Seed, Medra’s Lab, and DeepSeek V4’s Efficiency Push

*By VC Tech Radar • April 25, 2026*

Two fresh financings and several emerging teams point to durable wedges in autonomy, biotech automation, enterprise AI rollout, and agent security. The larger pattern is a sharper split between AI-native winners with real pull and crowded categories where usage, moats, and cost structure matter more than topline alone.

## Funding & Deals

- **Humble — $24M seed.** Eclipse led with Energy Impact Partners participating. The thesis is architectural: remove the cab, get 360° sensor coverage, cut weight, and optimize for 40- and 53-foot intermodal containers in dock-to-dock routes. CEO Eyal Cohen previously worked at Apple, Uber ATG, and Waabi, and co-founded Spark AI, acquired by John Deere in 2023; he says the team reached a prototype in under six months. [^1]
- **AstorInvest — $5M seed.** YC says Astor is building an AI investment advisor for everyday investors that connects to a brokerage account, analyzes the portfolio, and delivers personalized recommendations. Reported early traction: thousands of users connected more than $200M in assets less than two months after launch. [^2]
- **ComfyUI — $30M at a $500M valuation.** Led by Craft Ventures with PaceCap, Chemistry, TruArrow, and others, the round is a useful open-infrastructure read-through: ComfyUI says it has 4M users, 60k+ community-built nodes, and 150k+ daily downloads, and plans to spend on Comfy Cloud, collaborative workflows, local UX, ecosystem reliability, and day-one model support while keeping the platform open. [^3]

## Emerging Teams

- **Medra — robotic biology infrastructure with an AI scientist layer.** Michelle Lee’s company opened a 38,000 sq ft San Francisco lab where roughly 100 robotic arms run experiments continuously. Its core wedge is using computer vision and manipulation models on standard lab equipment, which Lee says can raise the share of biotech tasks that can be automated from 5% to 75%; the company frames itself as *TSMC for biology*. In one cited customer example, the AI scientist proposed adding a vortexing step and improved antibody binding from 0% to above 70%. [^1]
- **OpenWork — open-source enterprise rollout layer for AI.** YC describes OpenWork as an open-source alternative to Claude Cowork that supports existing agents, on-prem deployment, and any LLM provider. Early distribution is strong for this category: 14k GitHub stars and more than 150k downloads. YC highlighted founder Benjamin Shafii at launch. [^4]
- **Burrow — runtime security for agents from an operator who saw the failure mode firsthand.** The founder says he leads cloud security at a company processing $80B in annual payments and started building Burrow after an internal AI agent deleted a production S3 bucket with customer data. The product lets teams define agent controls in plain English, create alerts for agent deviation, and investigate or quarantine agents through its Lookout service. [^5]
- **Opero — small but measurable early traction in WhatsApp-native agents.** Three weeks in, the founder reports 25 users and 2 paying customers. The sharper product ideas are an LLM-evaluated signals system that only emits structured CRM webhooks when a user-defined condition is met, plus a self-improving loop where the owner answers one question and the agent stores the answer for future use; reported median turnaround is under 90 seconds. [^6]

## AI & Tech Breakthroughs

- **DeepSeek V4 pushes the long-context efficiency frontier again.** A technical deep dive describes V4 Pro as a 1.6T-parameter model with 49B active parameters and a new DSA hybrid attention architecture. At 1M context, the post says compute cost per token falls to 27% of V3.2 and KV cache to 10%, while LiveCodeBench reached 93.5, above GPT-5.4 at 91.7 in the cited comparison. The same post notes a weak spot in world knowledge, with SimpleQA-Verified at 57.9 versus Gemini 3.1 Pro at 75.6, and says DeepSeek describes itself as still 3-6 months behind the frontier there; the release is MIT licensed, with a 284B Flash model and 13B active parameters available. [^7]
- **Agentic workflows still look like a bigger lever than base-model upgrades.** Andrew Ng argues that iterative loops such as outlining, critiquing, researching, and revising produce much better work than one-shot prompting, and says his team found the gain from adding agentic workflow to GPT-3.5 on a coding benchmark was larger than the gain from moving from GPT-3.5 to GPT-4. AI Fund says it has been helping portfolio companies deploy these workflows, and Ng separately pointed to CrewAI, AutoGen, and LangGraph as agent workflow platforms to watch. [^8]
- **Runtime retrieval is starting to close the training-cutoff gap for coding agents.** Paper Lantern says its MCP server lets coding agents pull implementation guidance from more than 2M computer-science papers at runtime. In its 9-task benchmark, 5 tasks improved meaningfully; Python test generation moved from 63% bug catch to 87% using mutation-aware prompting from retrieved papers, and contract extraction improved from 44% to 76% using March 2026 papers that post-dated model training. Across the benchmark, 10 of the 15 most-cited papers were from 2025 or later. [^9]
- **Frontier model launches are not automatically collapsing specialist infra.** Sam Altman said GPT-5.5 and GPT-5.5 Pro are now available in the API, but LlamaIndex said its ParseBench testing still showed mixed OCR results: GPT-5.5 won on tables and visual grounding, lost on charts, content faithfulness, and semantic formatting in some comparisons, and came with materially higher per-page pricing than LlamaParse’s cited 1.25¢ per page. [^10][^11][^12][^11]

## Market Signals

- **Bifurcation is no longer theoretical.** SaaStr, citing Sapphire data, says enterprise software captured 52% of all VC funding in 2025, up from 41% in 2024, and that 80+ AI-native companies have already reached $100M+ ARR in under 18 months. AI-native operating profiles are diverging sharply from classic B2B, with cited ranges of 200-400% ARR growth, 130-200% NDR, 40-70% gross margins, and $1M-$5M ARR per employee. The same report says the top 10 private enterprise software companies are worth $1.93T, more than the pure SaaS public index at $1.88T, while public enterprise software has lost $2.4T in market cap since the October 2025 peak and pure SaaS trades at 3.1x NTM revenue. [^13]
- **Valuation froth looks concentrated, not universal.** Elizabeth Yin says the current bubble is strongest in AI infrastructure, where companies can reach millions in revenue in weeks or months, while crowded horizontal AI tools can attract few or no investors. She expects the frothiness to cool in 1-2 years as low-hanging use cases are exhausted, CAC rises, adoption slows, and investors pull back; her advice to founders is to optimize for business quality, not ease of fundraising. [^14][^15][^16][^17][^18][^19]
- **Due diligence is shifting from topline to engagement and moats.** Harry Stebbings argues that in B2B AI, MAUs, WAUs, and DAUs now matter more than revenue because flat usage can hide *stealth churn*, while Clement Delangue says investors have become too fixated on top revenue growth and need to return to moats, product quality, and differentiated usage. [^20][^21]
- **Seed investing still rewards volume, even in an AI-heavy cycle.** Newcomer, citing Dealroom, says YC leads seed-stage investing with 94 companies that later reached $100M+ revenue and now backs roughly 500-600 startups per year. SV Angel follows a similar small-check, wide-net approach with around 50-100 new investments annually, while Sequoia stands out as the most successful non-accelerator seed fund. The same Newcomer item notes Bill Gurley’s view that the AI boom remains heavily subsidized by VC cash. [^22]
- **Founders may be underestimating non-AI opportunities and overestimating coding as the bottleneck.** Paul Graham says AI is the biggest opportunity for startup founders, but non-AI ideas may be the most underpriced because others overlook them and some later become much larger through AI. Garry Tan’s related point is that in AI companies, deciding what to build, for whom, and how to get adoption is harder than writing the software. [^23][^24][^25][^26][^27]

## Worth Your Time

### Andrew Ng on agentic workflows
He argues that iterative agentic workflows can create larger gains than the GPT-3.5-to-GPT-4 jump on coding benchmarks, and pairs that view with falling training costs and better inference hardware. [^8]


[![Generative AI in the Real World: Andrew Ng on where AI is headed. It’s about agents.](https://img.youtube.com/vi/fSrk7QpQsFk/hqdefault.jpg)](https://youtube.com/watch?v=fSrk7QpQsFk&t=305)
*Generative AI in the Real World: Andrew Ng on where AI is headed. It’s about agents. (5:05)*


### Diana Hu on building an AI-native company
Useful for founders thinking about closed-loop companies, queryable orgs, software factories, and lean teams built around an intelligence layer rather than management middleware. [^28]


[![How To Build A Company With AI From The Ground Up](https://img.youtube.com/vi/EN7frwQIbKc/hqdefault.jpg)](https://youtube.com/watch?v=EN7frwQIbKc&t=76)
*How To Build A Company With AI From The Ground Up (1:16)*


### DeepSeek V4 primary materials
The [paper](https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro/blob/main/DeepSeek_V4.pdf) and [model collection](https://huggingface.co/collections/deepseek-ai/deepseek-v4). [^29][^30]

### Paper Lantern benchmarks and demo
Open benchmark repo and product demo: [GitHub](https://github.com/paperlantern-ai/paper-lantern-challenges) and [paperlantern.ai/code](https://paperlantern.ai/code). [^9]

### Learning mechanics
Kanjun highlighted a new paper that tries to name and organize an emerging scientific theory of deep learning, framing learning mechanics as the physics to mechanistic interpretability’s biology. Read the [paper](https://arxiv.org/pdf/2604.21691). [^31][^32]

### Elizabeth Yin’s valuation essay
Her thread links a fuller argument for why some AI valuations may be justified by revenue velocity while crowded horizontal categories may reset as competition intensifies. [Read it here](https://elizabethyin.com/2026/04/24/the-wide-path-why-most-current-valuation-dynamics-are-not-here-to-stay-and-what-will/). [^14][^15][^16][^17][^33]

---

### Sources

[^1]: [Weekly Dose of Optimism #190](https://www.notboring.co/p/weekly-dose-of-optimism-190)
[^2]: [𝕏 post by @ycombinator](https://x.com/ycombinator/status/2047775429420085738)
[^3]: [𝕏 post by @ComfyUI](https://x.com/ComfyUI/status/2047731693788995871)
[^4]: [𝕏 post by @ycombinator](https://x.com/ycombinator/status/2047752437327360176)
[^5]: [r/SideProject post by u/Valuable_Mud_474](https://www.reddit.com/r/SideProject/comments/1sur338/)
[^6]: [r/SideProject post by u/juancruzlrc](https://www.reddit.com/r/SideProject/comments/1sutdok/)
[^7]: [r/deeplearning post by u/Krosnt](https://www.reddit.com/r/deeplearning/comments/1sv0obo/)
[^8]: [Generative AI in the Real World: Andrew Ng on where AI is headed. It’s about agents.](https://www.youtube.com/watch?v=fSrk7QpQsFk)
[^9]: [r/deeplearning post by u/kalpitdixit](https://www.reddit.com/r/deeplearning/comments/1suhsfa/)
[^10]: [𝕏 post by @sama](https://x.com/sama/status/2047787124846653895)
[^11]: [𝕏 post by @jerryjliu0](https://x.com/jerryjliu0/status/2047803921037656389)
[^12]: [𝕏 post by @jerryjliu0](https://x.com/jerryjliu0/status/2047803923176522060)
[^13]: [Top 10 Learnings from Sapphire Ventures’ 2026 Software x AI Report: 80+ $100m ARR AI Startups, The Ultra Round is The New Normal, and Enterprise is 50%+ of VC Now](https://www.saastr.com/top-10-learnings-from-sapphire-ventures-2026-software-x-ai-report-80-100m-arr-ai-startups-the-ultra-round-is-the-new-normal-and-enterprise-is-50-of-vc-now)
[^14]: [𝕏 post by @dunkhippo33](https://x.com/dunkhippo33/status/2047799508361224318)
[^15]: [𝕏 post by @dunkhippo33](https://x.com/dunkhippo33/status/2047799509665661036)
[^16]: [𝕏 post by @dunkhippo33](https://x.com/dunkhippo33/status/2047799510961676396)
[^17]: [𝕏 post by @dunkhippo33](https://x.com/dunkhippo33/status/2047799512228417634)
[^18]: [𝕏 post by @dunkhippo33](https://x.com/dunkhippo33/status/2047799513507643804)
[^19]: [𝕏 post by @dunkhippo33](https://x.com/dunkhippo33/status/2047799517513195908)
[^20]: [𝕏 post by @HarryStebbings](https://x.com/HarryStebbings/status/2047316267858645088)
[^21]: [𝕏 post by @ClementDelangue](https://x.com/ClementDelangue/status/2047684789021384801)
[^22]: [Stakes Keep Getting Higher for the SpaceX IPO](https://www.newcomer.co/p/stakes-keep-getting-higher-for-the)
[^23]: [𝕏 post by @paulg](https://x.com/paulg/status/2047692744089313563)
[^24]: [𝕏 post by @paulg](https://x.com/paulg/status/2047693084230607010)
[^25]: [𝕏 post by @paulg](https://x.com/paulg/status/2047693636008087848)
[^26]: [𝕏 post by @garrytan](https://x.com/garrytan/status/2047707375663214732)
[^27]: [𝕏 post by @mert](https://x.com/mert/status/2047693832029077587)
[^28]: [How To Build A Company With AI From The Ground Up](https://www.youtube.com/watch?v=EN7frwQIbKc)
[^29]: [r/deeplearning post by u/RecmacfonD](https://www.reddit.com/r/deeplearning/comments/1sukv6z/)
[^30]: [r/deeplearning comment by u/SirRece](https://www.reddit.com/r/deeplearning/comments/1sukv6z/comment/oi1neuo/)
[^31]: [𝕏 post by @kanjun](https://x.com/kanjun/status/2047726150252933609)
[^32]: [𝕏 post by @learning_mech](https://x.com/learning_mech/status/2047723849874330047)
[^33]: [𝕏 post by @dunkhippo33](https://x.com/dunkhippo33/status/2047799518771519917)