# Petual and Glif Raise as DeepSeek and GStack Push the Agentic Stack Forward

*By VC Tech Radar • April 24, 2026*

New rounds landed in compliance automation, creative tooling, security, and ocean infrastructure, while YC launches added fresh traction in logistics, insurance, recruiting, and commerce. On the technical side, GStack became a reference point for agentic development workflows, DeepSeek pushed long-context efficiency, and the market kept shifting toward application-layer moats and AI-native efficiency.

## 1) Funding & Deals

- **PetualAI — $20M total.** Petual raised $20M total, including a $17M round led by a16z and a $3.2M round led by First Round, with participation from Cowboy Ventures, Elad Gil, and founders from Lyft and Opendoor [^1]. Founder Snir Kodesh previously led engineering at Retool and held senior engineering roles at Lyft [^2][^1]. The company applies agentic AI to SOX testing and internal audit, autonomously gathering evidence and generating auditor-ready workpapers in minutes rather than hours; it says S&P 500 and NASDAQ 100 customers see 68–80% efficiency gains [^1][^2][^1]. a16z’s thesis is that SOX is the entry point to a broader AI-powered control system for audit and compliance [^2].

- **Glif — $17.5M seed.** Glif announced a $17.5M seed led by a16z and USV [^3][^4]. It positions itself as a creative super agent that uses virtually every available AI model to create ads, marketing content, films, voiceovers, music, and more inside one conversation [^3][^4]. a16z’s angle is workflow consolidation: marketers often touch multiple gen-AI products in a single session, while Glif tries to collapse that sprawl into one agent; the founding team is described as strong across both technical and creative domains [^4].

- **Mindfort — $3M seed.** Mindfort raised a $3M seed to build autonomous security agents that run pentests on every CI/CD push, chain vulnerabilities into working proofs of exploit, and ship fixes as pull requests [^5].

- **Ulysses / The Ocean Company — $46M.** Ulysses raised $46M led by a16z American Dynamism to build ocean infrastructure and treat the ocean as a permanent economic fixture [^6]. Its stack combines $50,000 Mako AUVs, which the company says are 10x to 100x cheaper than incumbent models, with the Leviathan surface craft and Kraken launch/recovery platform for persistent subsea operations without crewed ships [^6]. Management says demand is already appearing at fleet scale, with one commercial customer requesting 10,000 vehicles and another at least 1,000 [^6].

## 2) Emerging Teams

- **Dayjob.** YC says Dayjob is building AI scheduling for waste trucks and is already at $496K ARR with 12 customers [^7].

- **Huscarl.** Huscarl is pitching an AI-native advisory model for corporate insurance buyers, with a claim of 30% savings on annual premiums and zero downside [^8].

- **Asendia AI.** Asendia AI builds AI recruiters for staffing agencies and enterprises by cloning top recruiters into agents that match, screen, and submit candidates 10x faster [^9]. YC highlighted founders @LajmiRihab and @zormati_ba at launch [^9].

- **Kinect.** Kinect is turning e-commerce stores into AI-powered storefronts that adapt to each visitor in real time and capture new buying-intent data for merchants [^10].

## 3) AI & Tech Breakthroughs

- **GStack is turning into a fast-growing open-source agent-coding toolkit.** Garry Tan’s toolkit turns Claude Code into an AI engineering team with specialist skills such as Office Hours, adversarial review, design-shotgun, browser QA, and parallel PR workflows [^11]. Tan says the scaffolding should stay thin, describes the result as a level-seven software factory rather than full autonomy, and says the repo was built three weeks earlier and had already crossed 70,000 GitHub stars [^11]. In practice, he says he runs 10 to 15 parallel Claude sessions and can land 10 to 50 PRs in a day across projects [^11].

> “Basically, I’ve written a lot of code in my career and I’m here to tell you we are in a completely new era of building software, the agent era.” [^11]

- **DeepSeek is making a new long-context efficiency push.** DeepSeek says V4 introduces token-wise compression plus DSA (DeepSeek Sparse Attention), delivering world-leading long-context efficiency with sharply lower compute and memory costs and making 1M context the default across official services [^12]. Adoption signals were immediate: DeepSeek-V4-Pro cleared 500+ likes on Hugging Face in 28 minutes and reached #1 trending after 43 minutes [^13][^14]. Early outside commentary described the first benchmark numbers as “astounding” and comparable to top frontier models, but verification was still underway [^15].

- **Replit is targeting the post-codegen security gap.** Replit argues AI has already automated most of the software development lifecycle, leaving DevSecOps as the next bottleneck, and launched Auto-Protect as a 24x7 vulnerability scanner for live apps [^16]. Replit frames it as the next step after Replit Agent: extending AI from building software into monitoring, security, and upkeep [^17].

- **Model-native interfaces are being prototyped.** Flipbook streams every pixel on screen directly from a model, with no HTML, layout engine, or code, and applies the same idea to video by generating each frame live without timelines, compositors, or render farms [^18][^19]. The prototype was built by @zan2434, @eddiejiao_obj, and @drewocarr [^18].

## 4) Market Signals

- **AI-written code has already crossed the 75% line in important startup and big-tech cohorts.** Paul Graham says YC startups passed 75% AI-written code at least one or two years ago [^20]. A separate data point cited this week says Google went from 0% to 75% AI-written code in roughly two years [^21].

- **More of the commercial logic is shifting to the application layer.** Latent.Space describes an agent-lab playbook: start with frontier models, specialize for a domain, then train or distill your own model once workload and user data justify the cost and latency gains [^22]. Aravind Srinivas makes the market version of the same argument: consumers buy products, pure model/API businesses are hard to defend as model gaps compress, and value accrues in the application layer and its harnesses [^23].

- **Efficiency heuristics are tightening for AI-native SaaS.** Team8 managing partner Alon Huri argues that AI-native companies are already hitting $2M to $5M in ARR per employee, and that headcount growing linearly with MRR is often a sign the company is acting more like an agency than software [^24]. His pre-PMF template is a four-person core team and an agent-first model where humans judge while agents execute tasks in sales, customer success, and ops [^24].

- **LLMs are becoming both a distribution surface and a monitoring surface.** ReqRes says it has 48,000 registered users and 300 daily signups with no paid marketing; ChatGPT is already its third-largest traffic source, and it says 333 universities teach with the product while 100+ engineers at one Big 4 IT services firm signed up on their own [^25]. Lima is building around the inverse problem: tracking how brands are mentioned across ChatGPT, Claude, Grok, Google AI, and Perplexity, with prompt suggestions plus prompt and citation breakdowns [^26].

- **Founders are still distinguishing task automation from AGI.** Garry Tan called openclaw “highly effective task-automation” and “genuinely impressive and useful,” but said AGI would require zero-shot identification and solution of novel, unscoped problems without human setup [^27][^28].

## 5) Worth Your Time

- **GStack walkthrough:** [How to Make Claude Code Your AI Engineering Team](https://www.youtube.com/watch?v=wkv2ifxPpF8) shows Office Hours, adversarial review, design-shotgun, browser QA, and parallel PR workflows in one system [^11].


[![How to Make Claude Code Your AI Engineering Team](https://img.youtube.com/vi/wkv2ifxPpF8/hqdefault.jpg)](https://youtube.com/watch?v=wkv2ifxPpF8&t=40)
*How to Make Claude Code Your AI Engineering Team (0:40)*


- **Ocean thesis:** [The Great Blue Frontier](https://www.notboring.co/p/the-great-blue-frontier) lays out Ulysses’ thesis, the Mako/Leviathan/Kraken stack, and the early demand signal for 1,000–10,000 vehicle fleets [^6].

- **Agent-labs framework:** [AIE Europe Debrief + Agent Labs Thesis](https://www.latent.space/p/unsupervised-learning-2026) covers the frontier-model to domain-specialized to in-house-model playbook, coding-market scale, and the idea of zero-human-review “dark factories” [^22].

- **Security agents:** [Mindfort’s seed announcement](https://www.mindfort.ai/blog/seed-announcement) outlines a product that moves from autonomous pentesting to shipping fixes as pull requests [^5].

- **Production agent architecture:** [Max Agency with ListenLabs CTO Florian Jue](https://www.youtube.com/watch?v=YTTH-0XXEBE) discusses self-reviewing subagents, sandboxes, abstractions, and response analysis at scale [^29].

---

### Sources

[^1]: [𝕏 post by @snirkodesh](https://x.com/snirkodesh/status/2047322848004559176)
[^2]: [𝕏 post by @a16z](https://x.com/a16z/status/2047337107346301362)
[^3]: [𝕏 post by @fabianstelzer](https://x.com/fabianstelzer/status/2047359946702880920)
[^4]: [𝕏 post by @a16z](https://x.com/a16z/status/2047366048916365676)
[^5]: [𝕏 post by @ycombinator](https://x.com/ycombinator/status/2047367987129360645)
[^6]: [The Great Blue Frontier](https://www.notboring.co/p/the-great-blue-frontier)
[^7]: [𝕏 post by @ycombinator](https://x.com/ycombinator/status/2047330849922904479)
[^8]: [𝕏 post by @ycombinator](https://x.com/ycombinator/status/2047374948272951608)
[^9]: [𝕏 post by @ycombinator](https://x.com/ycombinator/status/2047344753579073745)
[^10]: [𝕏 post by @ycombinator](https://x.com/ycombinator/status/2047359851735486471)
[^11]: [How to Make Claude Code Your AI Engineering Team](https://www.youtube.com/watch?v=wkv2ifxPpF8)
[^12]: [𝕏 post by @deepseek_ai](https://x.com/deepseek_ai/status/2047516936289017964)
[^13]: [𝕏 post by @ClementDelangue](https://x.com/ClementDelangue/status/2047519396738830453)
[^14]: [𝕏 post by @ClementDelangue](https://x.com/ClementDelangue/status/2047535160187330573)
[^15]: [𝕏 post by @bindureddy](https://x.com/bindureddy/status/2047515312434934166)
[^16]: [𝕏 post by @amasad](https://x.com/amasad/status/2047411360993034262)
[^17]: [𝕏 post by @amasad](https://x.com/amasad/status/2047411405171708349)
[^18]: [𝕏 post by @zan2434](https://x.com/zan2434/status/2046982383430496444)
[^19]: [𝕏 post by @c_valenzuelab](https://x.com/c_valenzuelab/status/2047376679123783948)
[^20]: [𝕏 post by @paulg](https://x.com/paulg/status/2047323487405019458)
[^21]: [𝕏 post by @deedydas](https://x.com/deedydas/status/2046986776439660997)
[^22]: [AIE Europe Debrief + Agent Labs Thesis: Unsupervised Learning x Latent Space Crossover Special \(2026\)](https://www.latent.space/p/unsupervised-learning-2026)
[^23]: [Aravind Srinivas & Edwin Chen: The $1B Bootstrap, Apple's AI Edge, and Benchmarks | TWiAI E10](https://www.youtube.com/watch?v=Xwjr_jxGFG8)
[^24]: [r/SaaS post by u/AlonHuri](https://www.reddit.com/r/SaaS/comments/1su5l4x/)
[^25]: [r/SaaS post by u/Comprehensive_Rope25](https://www.reddit.com/r/SaaS/comments/1stwa5w/)
[^26]: [r/SideProject post by u/Salescamp](https://www.reddit.com/r/SideProject/comments/1stzb7p/)
[^27]: [𝕏 post by @iyermallika](https://x.com/iyermallika/status/2047496665091678480)
[^28]: [𝕏 post by @garrytan](https://x.com/garrytan/status/2047498710901555205)
[^29]: [𝕏 post by @hwchase17](https://x.com/hwchase17/status/2047337303287476515)