# Campfire’s Series B, BioStack’s Revenue Jump, and Verification as AI’s Next Layer

*By VC Tech Radar • May 26, 2026*

Campfire provides the clearest financing signal, while BioStack, Callab_AI, and Mount show where early-stage AI companies are finding product wedges. Across the set, the stronger pattern is a move toward verifiable AI, local inference, and control layers around autonomous systems.

## Funding & Deals

- **Campfire** recently raised a **Series B led by Accel and Ribbit Capital**. The thesis is an **AI-native ERP** for high-growth tech companies that automates accounting, taxes, and investor reporting; YC also said Campfire has **more than doubled ARR each quarter since Q4 2024** and now has **100+ employees** after closing a **$35 million Series A in June 2025 with 12 people** [^1].
- **Conifer** says it has funding to build an **open-source local inference runtime for Apple Silicon**. The five-person Princeton team is building it in **Rust with handwritten kernels**, says it is **ahead of llama/mlx on small models**, and is using a **100-user free beta** to surface bugs and tool needs [^2].

## Emerging Teams

- **BioStack** is the strongest early traction signal in the set. It builds **simulation environments where healthcare AI models practice on real clinical data**, converting messy records, lab tests, notes, and long-horizon outcomes into **data, evals, rewards, and benchmarks**; YC said revenue moved from **six figures to seven figures in just the last few weeks** [^3]. YC identified the founders as **@sanatmishra7** and **@patwa_parth** [^3].
- **Callab_AI** is attacking a large legacy-integration wedge. The company connects AI voice agents directly to **on-prem PBX systems** such as **Cisco UCM** and **Mitel**, avoiding migration in a market where **58% of the $400B call center industry** still runs on-prem [^4]. YC identified the founders as **@haithemkchaou** and **@chehir_dh** [^4].
- **Mount** is notable because it turns AI-agent risk into an insurable product. Its pitch is to **secure autonomous-agent workflows**, **measure residual risk**, and **transfer that risk through insurance built specifically for AI agents** so companies can use agents without carrying the full downside alone [^5]. YC identified the founders as **@johnbachm** and **@fabeamherd** [^5].

## AI & Tech Breakthroughs

- **Delta Attention Residuals** is the clearest research signal in this batch. Instead of routing over cumulative hidden states, it routes over deltas, which the authors say avoids routing collapse in deep layers and produces **1.8x sharper cross-layer routing** [^6]. Reported results include **1.7-8.2% lower validation PPL from 220M to 7.6B**, **drop-in fine-tuning of pretrained models that beats baseline on 8 benchmarks**, and **0.008% parameter overhead at 8B** [^6].
- **Small local models are getting more practical.** Garry Tan said **Qwen2.5-7B Instruct** is at **GPT-3.5-turbo level** and argued that even if local models are not the default, **every device will need one** as a fallback when connectivity fails [^7][^8]. Conifer is building toward that future with a runtime for **fully local agents** that can access files and apps under **OS kernel enforcement** [^2].
- **AI-guided gene editing** remains a frontier category. Nathan Benaich highlighted **ProfluentBio**'s work on designing **large gene insertions** and **fine-scale editing with AI** [^9].

## Market Signals

- **Verification, governance, and risk transfer are emerging as a distinct AI layer.** Vinod Khosla called **autoformalization** the next critical frontier and said founders should work on areas where AI is weak [^10][^11]. In the same direction, Orygent is building a governed enterprise work layer around **trust, approvals, audit trails, role-based authority, and verifiable AI**, while Mount focuses on **security plus insurance** for autonomous agents [^12][^13][^5].
- **Local inference is shifting from niche feature to resilience layer.** Garry Tan said local models will not be the default, but every device will need one as an **emergency generator** when connectivity drops [^8]. Conifer's funding and beta around Apple Silicon local inference is one startup expression of that view [^2].
- **The software labor debate is becoming more explicit.** Bindu Reddy argued that engineers are producing **10-100x more code**, that layoffs will continue at companies with large engineering teams, and that the current status quo is unsustainable because of resulting instability in large codebases and teams [^14].

## Worth Your Time

- **Delta Attention Residuals:** [paper](https://arxiv.org/abs/2605.18855) and [code](https://github.com/wdlctc/delta-attention-residuals-code). The work reports **1.7-8.2% lower validation PPL** from **220M to 7.6B** with **0.008% parameter overhead at 8B** [^6].
- **World-model explainer:** [drops.mts.now/world-model](http://drops.mts.now/world-model). It covers what world models are, how they work, and what **DreamZero** and **Agora-1** are building; Marc Andreessen amplified it on X [^15][^16].
- **Campfire founder thread:** [X post](https://x.com/ycombinator/status/2058949824616231203). YC says the discussion covers launching the first paying version as a **Google Sheet**, pulling customers off **NetSuite** with **four employees**, and founder-led sales through **Series A** [^1].
- **BioStack launch page:** [YC launch](https://www.ycombinator.com/launches/QUp-biostack-platforms-realistic-healthcare-simulation-environments). BioStack says it turns messy clinical data into post-training loops for healthcare AI, and YC says revenue moved from **six figures to seven figures** in weeks [^3].
- **Conifer beta and feedback:** [site](http://conifer.build/) and [waitlist](https://conifer.build/feedback). The team says it is building an **open-source Apple Silicon runtime** and is taking **100 users** into a free beta [^2].

---

### Sources

[^1]: [𝕏 post by @ycombinator](https://x.com/ycombinator/status/2058949824616231203)
[^2]: [r/artificial post by u/No_Elephant_7530](https://www.reddit.com/r/artificial/comments/1tnnaa6/)
[^3]: [𝕏 post by @ycombinator](https://x.com/ycombinator/status/2058990537555018228)
[^4]: [𝕏 post by @ycombinator](https://x.com/ycombinator/status/2059031556942164376)
[^5]: [𝕏 post by @ycombinator](https://x.com/ycombinator/status/2058956261132222917)
[^6]: [r/MachineLearning post by u/Mediocre-Ad5059](https://www.reddit.com/r/MachineLearning/comments/1tndn5b/)
[^7]: [𝕏 post by @garrytan](https://x.com/garrytan/status/2059007262182785432)
[^8]: [𝕏 post by @harjtaggar](https://x.com/harjtaggar/status/2058942843172139211)
[^9]: [𝕏 post by @nathanbenaich](https://x.com/nathanbenaich/status/2059053725050659322)
[^10]: [𝕏 post by @sathyanellore](https://x.com/sathyanellore/status/2058965576564019697)
[^11]: [𝕏 post by @vkhosla](https://x.com/vkhosla/status/2058976857345954154)
[^12]: [𝕏 post by @34marcopascha](https://x.com/34marcopascha/status/2058989889484620157)
[^13]: [𝕏 post by @vkhosla](https://x.com/vkhosla/status/2059005713385656371)
[^14]: [𝕏 post by @bindureddy](https://x.com/bindureddy/status/2059050001439858791)
[^15]: [𝕏 post by @MTSlive](https://x.com/MTSlive/status/2058995565204316346)
[^16]: [𝕏 post by @pmarca](https://x.com/pmarca/status/2059026564336284027)