惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

D
Darknet – Hacking Tools, Hacker News & Cyber Security
NISL@THU
NISL@THU
S
Securelist
O
OpenAI News
S
Security Affairs
Cyberwarzone
Cyberwarzone
T
Threatpost
Simon Willison's Weblog
Simon Willison's Weblog
The Last Watchdog
The Last Watchdog
L
LINUX DO - 最新话题
C
Cisco Blogs
PCI Perspectives
PCI Perspectives
SecWiki News
SecWiki News
S
Secure Thoughts
GbyAI
GbyAI
I
Intezer
AWS News Blog
AWS News Blog
F
Fortinet All Blogs
I
InfoQ
阮一峰的网络日志
阮一峰的网络日志
Google Online Security Blog
Google Online Security Blog
www.infosecurity-magazine.com
www.infosecurity-magazine.com
A
About on SuperTechFans
S
Schneier on Security
P
Proofpoint News Feed
雷峰网
雷峰网
Exploit-DB.com RSS Feed
Exploit-DB.com RSS Feed
小众软件
小众软件
H
Heimdal Security Blog
Microsoft Security Blog
Microsoft Security Blog
K
KPMG report finds enterprise disconnect between AI and its ROI | CIO
T
The Exploit Database - CXSecurity.com
T
Threat Research - Cisco Blogs
V
V2EX
L
Lohrmann on Cybersecurity
Security Latest
Security Latest
A
Arctic Wolf
Apple Machine Learning Research
Apple Machine Learning Research
H
Hacker News: Front Page
Cisco Talos Blog
Cisco Talos Blog
Webroot Blog
Webroot Blog
T
Tenable Blog
MyScale Blog
MyScale Blog
博客园 - 司徒正美
S
SegmentFault 最新的问题
Y
Y Combinator Blog
腾讯CDC
Hacker News: Ask HN
Hacker News: Ask HN
M
MIT News - Artificial intelligence
G
GRAHAM CLULEY

Latent.Space

[AINews] Claude Opus 5: Fable-level performance at Opus price (half Fable) [AINews] Black Forest Labs FLUX 3 - Multimodal Flow Models that beat Seedance 2.0, Gemini Omni and Grok Imagine, and FLUX-mimic video-action robotics model [AINews] "Laguna S 2.1 Released: Cheaper than Deepseek v4 Flash, Better than V4 Pro" Inside the Model Factory — Eiso Kant, Poolside AI [AINews] AI Cybersecurity becomes top of mind 🔬Causal Models Need Causal Data - Xaira’s X-Cell model for Drug Discovery (Bo Wang & Ci Chu, Chief Discovery Officer & Chief AI Scientist) [AINews] not much happened today [AINews] not much happened today [AINews] Kimi K3 2.8T-A50B: the largest open model ever released; Opus 4.8-class at Sonnet 5 pricing 🔬 The Lab of the Future Should Feel Like a Data Center — Andy Beam & Rafa Gómez-Bombarelli, Lila Sciences [AINews] Thinky's Inkling: 975B-A41B multimodal, new best American Apache 2.0 open model (with Inkling-Small, 276B-A12B) [AINews] not much happened today 5 Trends That Defined AI Engineering at World’s Fair 2026 [AINews] Codex usage up >10x in 6 months to 7M users, +1M in the past ~day; did Codex overtake Claude Code?? [AINews] not much happened today [AINews] OpenAI launches GPT 5.6 Sol/Terra/Luna, Codex becomes ChatGPT superapp [AINews] SpaceXAI launches Grok 4.5, first Opus-class model post Cursor acquisition Why AI Infrastructure must evolve for Agent Experience — Akshat Bubna, Modal CTO [AINews] Lilian Weng summarizes 35 papers on Harness Engineering for RSI [AINews] The Field Guide to Fable AIEWF Daily Dispatch: The great loops debate and the state of AI engineering Vercel's Andrew Qu on why agents are a new kind of software The website of the future may assemble itself for every visitor Skill engineering and the case against one-shot AI design [AINews] not much happened today AIEWF Daily Dispatch: Autoresearch and the tension between AI and human agency Autoresearch: The feedback loop behind self-improving agents How Cursor deploys AI inside the enterprise 🔬 The Coolest Diffusion Research Isn't in LLMs — Evan Feinberg & Sergey Edunov, Genesis Molecular AI AIEWF Daily Dispatch: Loops, Software Factories & Forward Deployed Engineers [AINews] Sonnet 5 today, and Fable 5 tomorrow Forward Deployed Engineers and the future of software engineering Ahmad Osman on why local AI is catching up [AINews] not much happened today [AINews] OpenAI GPT-5.6 Sol / Terra / Luna — restricted to trusted partners [AINews] OpenAI reports median internal Codex output tokens grew 56x in Research, 32x in Customer Support, 27x in Engineering, and 13x in Legal since November 2025. [AINews] It's Meta-Harness Summer Why the Frontier Ecosystem must be Open — Matei Zaharia and Reynold Xin, Databricks [AINews] Claude Tag: Multiplayer, Proactive, Persistent Agents in Slack [AINews] SpaceX is already a $28B/yr Neocloud Red-Teaming after Mythos — Zico Kolter & Matt Fredrikson, Gray Swan How to AIE Good [AINews] not much happened today [AINews] GLM-5.2 is the real deal; Z.ai forecasts Open Fable by EOY The Professor of Outputmaxxing — Anjney Midha, AMP [AINews] Midjourney Medical: scan your organs like you step on a scale 🔬 The Self-Driving Lab — Joseph Krause, Radical AI [AINews] GLM-5.2: the top Frontend Coding model in the world, IndexShare for Speculative Decoding [AINews] Satya on Loopcraft: Building Frontier Ecosystems [AINews] Fable and Mythos officially too dangerous to release [AINews] Loopcraft: The Art of Stacking Loops [AINews] Loopcraft: The Art of Stacking Loops [AINews] Open Models, Model Labs vs Agent Labs, and What's Untrainable — Sarah Guo [AINews] Anthropic Claude Fable 5 — Mythos but Safe, with Controversial Terms [AINews] FrontierCode: Benchmarking for Code Quality over Slop [AINews] not much happened today How to Stop Shipping Low-Quality RL Environments (with Examples) [AINews] not much happened today Reality: The Final Eval — Lukas Petersson and Axel Backlund of Andon Labs [AINews] Reve 2 and Ideogram 4: Layouts in Imagegen 🔬Scaling Past Informal AI - Carina Hong, Axiom Math ⚡️Satya Nadella: No Priors x Latent Space Crossover Special at Microsoft Build [AINews] Microsoft Build: MAI-Thinking-1 and MAI Family models GitHub's plan for Agents — Kyle Daigle, GitHub [AINews] NVIDIA Cosmos 3, Nemotron 3 Ultra, and RTX Spark Why Video Agent models are next — Ethan He, xAI Grok Imagine [AINews] Founders and Forward Deployed Engineers [AINews] Anthropic raises $965B Series H, releases Opus 4.8 and Dynamic Workflows/ultracode The Age of Async Agents — Cognition's Walden Yan & OpenInspect's Cole Murray [AINews] Cognition raises $1B in $26B Series D 🔬 ESMFold2: The Bitter Lesson is Coming for Proteins - Alex Rives, BioHub [AINews] New AI Infra decacorns: Fireworks, Baseten (with OpenRouter on the way) [AINews] All Model Labs are now Agent Labs [AINews] New AI Infra unicorns: Exa, Modal, TurboPuffer Giving Agents Computers — Ivan Burazin, Daytona [AINews] OpenAI GPT-next disproves 80 year old Erdős planar unit distance problem for under $1000 Railway: The Agent-Native Cloud — Jake Cooper [AINews] Google I/O 2026: Gemini 3.5 Flash, Omni (NanoBanana for Video), Spark (background agents), and Antigravity 2.0 [AINews] How to land a job at a frontier lab (on Pretraining) The Autonomous Drone Tech Stack & Economics of Drones — Yaroslav Azhnyuk, The Fourth Law & Guest Host Noah Smith, Noahpinion [AINews] Cerebras' $60B IPO: Slowly, then All at Once [AINews] Everything is Conductor AI-Native Healthcare: 100M Doctor Visits, 10–20 Hours Saved, Prior Auth in Minutes — Janie Lee & Chai Asawa, Abridge [AINews] Codex Rises, Claude Meters Programmatic Usage [AINews] The End of Finetuning [AINews] Thinking Machines' Native Interaction Models - TML-Interaction-Small 276B-A12B - advances SOTA Realtime Voice and kills standard VAD
Warp CEO Zach Lloyd on why software factories are the next phase of coding
Richard MacManus · 2026-07-01 · via Latent.Space
Warp founder Zach Lloyd in the AI Engineer World’s Fair expo hall.

I’ve been covering Warp for a couple of years now, and its rapid evolution from a command-line interface tool to a software factory platform has been fascinating to watch. The company began in the pre-ChatGPT days, in mid-2021, as a Rust-based terminal. Then when AI hit, it turned into a terminal with integrated coding agents.

But the competition among CLI tools has dramatically increased in recent years, including from Claude Code, Codex CLI, and Gemini CLI — three products backed by massive tech companies. This likely led to Warp’s decision to open-source its core CLI tool in April this year.

I’m a Warp user myself, finding it a much more sophisticated tool than my native Mac CLI. But I also admire the company’s ability to adapt to the times — a trait I spotted in CEO Zach Lloyd during my first interview with him a couple of years ago. So I was keen to catch up with him at the AI Engineer World’s Fair this week, where he presented a keynote session on software factories, the new term for orchestrating a team (ahem, a factory) of agents.

Warp has a new agent orchestration platform called Oz. It’s the company’s answer to what Lloyd believes is an industry transition, from engineers working interactively with agents to automated systems that continuously triage, implement, review, verify and monitor software changes. Oz is intended to connect multiple models and coding harnesses across local environments and isolated cloud sandboxes, while fitting into tools developers already use.

I spoke to Lloyd just after he made his presentation on-stage, which you can view on YouTube — it’s a good primer to what software factories are. In our one-on-one discussion, we get into the reasons Warp made its software factory pivot, how Lloyd came up with the term (independently, it seems, from similar companies — like Factory), and why he expects most significant software projects to operate some form of automated factory within the next year.

Latent Space: When did you first come across the term “software factory,” and what attracted you to the concept?

Zach Lloyd: I can’t remember exactly when I started conceiving of it in those terms, but it was within the last six months, as the ability to automate software development became more complete.

We started with more one-off automation: run an agent in the cloud. A lot of platforms began there. Then it became: run an agent in the cloud on a timer.

The next question was, what is the most valuable loop to automate? The answer is basically the main loop of software engineering: triage, specification, implementation, review, verification, shipping and monitoring.

We began building toward this cloud-automation vision about a year ago, before we started building Oz. Over the past few months, the industry has also begun coalescing around the ‘factory’ term. There is an entire software-factory track at this conference.

It is literally what we are gearing our product around. In the next version of Oz, you will set up your factory, see what it looks like and manage the factory floor.

But I don’t care that much whether the term sticks. The essential shift is from interactive development to automated development. “Factory” is a useful metaphor for that.

Latent Space: In your presentation, you showed a software-factory stack containing several of your own products. Is Warp’s plan to provide the tools that make up that stack?

Lloyd: Yes. When you enter Oz, our cloud-agent platform, you will be walked through setting up a factory.

You choose your repositories, the parts of the software lifecycle you want to automate, and the points where humans should be brought into the loop. Different organizations and codebases will have different preferences. Do you fully automate code review? Do you have humans review certain high-risk changes?

The system then starts creating the loop. It might pull issues from Jira or Linear, let people submit them through Slack or Teams, and allow developers to redirect an agent from GitHub.

What is interesting from a product perspective is that most of the factory is not necessarily a new interface. It is an integration into people’s existing workflows. That is how we are conceiving it, at least.

Latent Space: When I first wrote about Warp, it was building a modern terminal. Code is still important now, but increasingly it is being produced by agents. It looks like Warp has broadened its product vision accordingly...

Lloyd: One hundred percent. A good way to think about it is that the company’s mission has stayed the same since we founded it. It has always been about empowering developers and companies to ship better software more quickly.

The product has evolved tremendously. It began as a modern version of the terminal, before the current AI wave. The next iteration was a terminal with agents built into it, which we are still investing in and which we have now open-sourced.

But the world keeps changing. The underlying AI improves so quickly that my view of the future is what I described in the talk: the interactive component is going to become less important.

As a company, you will want a central place where software gets built and where you can measure the efficiency of that process. I’m not afraid to redirect what the product becomes. As the underlying technology gets better, companies that do not adapt are going to be left behind.

Latent Space: The word “factory” may be off-putting to some developers, given its connotations with mechanism and rote work. What feedback have you received from AI engineers about this pivot?

Lloyd: The concept resonates strongly with the economic buyer — the person running the engineering team.

For an individual engineer, it can sound mechanized and uncreative. They may think: “I enjoy coding. Why would I want to work in a factory?”

One point I tried to communicate in the talk is that this will become a new engineering discipline. I think it can be extremely interesting if you view the job as meta-engineering: building the system that builds the product.

It uses many of the same problem-solving skills. You are asking why an agent performs one task well and another poorly. How should you adjust its feedback? What context does it need? How should the workflow change?

But, for better or worse, the power of these systems and their ability to accelerate software development are so great that writing everything by hand is not going to make sense for much longer.

Latent Space: Another trend at the conference is forward-deployed engineering, which often combines aspects of product management, consulting and traditional engineering. How does that fit into the software-factory model?

Lloyd: Standing up a software factory potentially involves integrating with a large number of existing systems, depending on the company.

The factory will work most effectively when it has context from those systems and is integrated throughout the organization’s workflow. A lot of forward-deployed engineering work in this area is effectively a transformation project.

It requires real engineering from someone who understands how to configure and deploy one of these systems. We do some of that, and some of our competitors do as well.

I don’t know what the final state will look like. Warp is approaching it more as a platform business than a services business. But there is certainly a business today in sending smart people into a company to transform its workflow using these products.

Latent Space: I use Warp as my terminal, including for some coding tasks. What happens to the original Warp CLI product in the software-factory era?

Lloyd: When we open-sourced Warp, we put the repository under the control of Oz. We built a software factory around the open-source project, using our own factory platform.

We are still trying to improve Warp as much as possible. We are doing it with the community, and we are doing a lot of it with agents. In that sense, Warp is a test bed for the factory concept.

But it is also a product used by almost a million developers, many of whom rely on it as their primary development environment. We use it constantly ourselves, and we still have internal engineers whose job is to improve it. We are simply approaching that work with a factory mindset.

Latent Space: What do you expect the next year to look like, in terms of adoption of software factories?

Lloyd: This will not happen all at once. Engineers are not going to wake up one morning and discover that a software factory has replaced their jobs.

Companies will start with specific use cases, certain types of issues or lower-risk repositories. Those are places where they may be comfortable not having a human review every single line of code.

They will see how it performs. Then the engineering challenge becomes: instead of merging 20% of pull requests automatically, can we get to 30%, 40%, 50% or 60%?

There will still be a remaining percentage of work done by people because it is too difficult, ambiguous or dependent on greenfield thinking.

But I think this shift will happen over the next year. My prediction is that every significant software project will have some engine of code — something resembling a factory — continuously driving it forward.

It will become similar to GitHub or CI/CD: a standard part of how serious software projects operate. I would be surprised if that did not happen.

Latent Space: There are thousands of AI engineers at this conference. What should they do to prepare for this shift?

Lloyd: Instead of only building the product directly, try building some automation toward a factory and see what it feels like.

Suppose you want an agent to implement incoming user issues automatically. What is involved in making that work? What prevents you from adopting it?

Perhaps code review is the bottleneck. Perhaps the agent is making changes, but you cannot clearly see what it did. You only discover those problems by trying to build the loop.

Get out of the mindset of building everything by hand. Find an annoying part of your job and try to create a loop that handles it for you using a factory approach.