You walk into a restaurant. Open the Blackbird app to check in and pay. Earn points.
That's @Blackbird.
Here's why we're excited about how they're improving the restaurant experience for customers and restaurant operators.
We've shipped 40+ AI engagements and have led FDE efforts in the last 12 months... here's the actual AI problems we've been solving for enterprises
1. Compressing long, multi-system business processes. Mapping a process that lives across email, Excel, SharePoint, ERPs, and DAMs, then collapsing it with agentic workflows.
2. Document and unstructured data extraction. Pulling structured data out of messy inputs at scale.
3. Internal knowledge and search across fragmented systems. Querying institutional knowledge that lives in calls, memos, CRM, and docs.
4. Customer-facing AI agents (chat, voice, support). Production agents handling end-customer interactions.
5. Agentic commerce. Catalogs, checkouts, and brand surfaces ready for AI agent traffic.
6. Computer vision in physical workflows and applied to specific operational decisions.
7. AI in regulated/healthcare environments. On-prem, HIPAA, data sovereignty work where the AI has to live where the data lives.
8. AI governance and internal AI sandboxes. Building the safe environment where staff can use AI compliantly.
9. Engineering productivity and software factories. AI inside the SDLC, ticket to PR.
10. Custom model and platform builds for AI startups. Helping AI companies build their own products and stand up FDE arms.
11. Evals, benchmarks, and RL environments. Measurement infrastructure that decides whether agents are safe to ship.
12. Data and ML infra for AI workloads. The foundation under everything else: pipelines, GPU clusters, IaC.
13. AI advisory at the strategy and PE portfolio level. Helping investors and operators decide where AI fits.
New Anthropic research: Natural Language Autoencoders.
Models like Claude talk in words but think in numbers. The numbers—called activations—encode Claude’s thoughts, but not in a language we can read.
Here, we train Claude to translate its activations into human-readable text.
shipping a new meta-loop: analyze past agent sessions → extract failure patterns → auto-build skills from them → eval & iterate. agents that get smarter from their own mistakes 🔄
AI doesn't kill elite agencies and services firms. It kills average ones.
Here's what this means for @Lazer_HQ and how we've positioned our team over the last year to take advantage of all the advancements in AI that allow us to do more for our clients and partners.
Back in January I told Eng two things:
1. Delete your IDE
2. Stop writing code
And build only through agents.
In a few weeks, we:
• Built 30+ internal tools to 10x the way we work
• Created a deep library of agents + skills to kill repetitive work
• Formed “agent councils” for PR and app perf reviews
• Shipped multi-month projects in ~1 day
It was a clear mental shift to focus us on the things that matter most:
- Upstream intent.
- Downstream validation.
Engineering has always been about building with intent and judgement.
The code was just a medium for expression. Now agents are that medium.
Rip the bandaid off.
Today we're excited to announce CommerceBench, an eval harness and benchmark for real-world agentic commerce, alongside reinforcement learning (RL) environments
CommerceBench evaluates whether AI agents can reliably complete end-to-end commerce workflows across storefronts and platforms
Until now, there's been:
- No commerce-specific benchmark
- No credible, repeatable way to measure true end-to-end commerce performance
- No safe, controllable RL environment to post-train them when they fail
As AI begins to mediate more commerce interactions, this gap becomes critical
Now with CommerceBench, we can measure:
- Is agentic commerce actually "good enough yet" compared to humans
- Which models/labs perform better on success rate, reliability, efficiency, and cost
- Which platforms are most agent-friendly (e.g. Shopify, Salesforce, etc)
- Which interaction methods are best (e.g. Browser, MCP, UCP)
Our goal is for CommerceBench to become the default substrate for evaluating these agents and for post-training
If you're a researcher or frontier lab thinking about commerce agents, reach out, we'd love to talk ❤️
https://t.co/n5rerXFzqe
this is exactly right, anyone in your company should be able to ship code / PRs in natural language and from the places they already work (Slack, Linear, etc..)
without needing to understand how to use a CLI, git, or a local dev environment.
I predict that background agents / cloud agents / remote agents, whatever we call them, will be pushing the majority of code by the end of 2026.
https://t.co/oOJhfTU3hz
Forward deployed AI engineering is something we’re already doing a lot of at @Lazer_HQ
It’s only going to grow as enterprises integrate AI tools more and more
Tl;dr: We’re focusing the Base app to be trading-first to drive demand and distribution for every asset and to be the best app for whatever you do in the onchain economy.
Since announcing the Base app in July, hundreds of thousands of you have used the app to create, trade, save, spend, and build. Seeing the adoption has been incredible. We've also heard clear feedback about what's landing and isn't. Three themes stand out:
- The app felt overly focused on social. It came across as too close to web2, and didn’t show support for the full breadth of assets that people want to trade.
- Everyone wants more high quality assets. In general there is a desire to engage with and trade high quality assets. This is the most important opportunity as we bring capital markets onchain.
- The feed needs to surface everything: Having a feed of what's happening onchain is a good idea, but it needs to surface apps, stocks, predictions, and every asset class (with social tokens are just one of many).
In a world where everything is tokenized and tradeable, the single most valuable thing we can do is drive demand and distribution to everyone. That’s exactly what the Base app is going to do. We’re going to make the Base app the best place to trade and use every asset.
Concretely this means:
1. We’re going to build for trading first. Having trading as our primary focus will help us bring demand and capital for all rapidly growing asset classes in the economy.
2. We’re going to bring more high quality assets onchain. To best serve the trading use case, we’re going to make it so everything is tradable in the app — protocols, apps, stocks, predictions, memes, and yes creators too.
We’re going to lean into a finance-first UX. We be
3. We’re going to lean into a finance-first UX. We believe it makes more sense to layer social features on top of finance, than the other way around. This means we'll continue to experiment with features like copy-trading, feed-trading, and leaderboards.
This is going to be hugely additive to the Base economy because it's going to drive more capital and users to every asset and app.
Base app will be the best self-custodial wallet to trade and use every asset, globally accessible, with fast, simple onboarding for everyone, everywhere. Base chain will continue to be the best chain to build anything, now supercharged with even more distribution.
We’re building this together, in the open, and seeing how people use the app keeps teaching us what matters most. Thank you for the continued feedback.
Stay based.
People generally don't go after their most ambitious ideas. But that's what creates the most value. Plus there's fewer people who play in that space.
Go for the hard thing.
2025 has been our busiest year so far ❤️
We did 150+ unique total projects
48 crypto, 46 commerce, 34 B2B/SaaS, 16 consumer, 15 AI, 8 mobile, 7 fintech, 4 healthcare
And we're now at 170+ people, with 0 investors and well into 8 figures in revenue 🚀
In the moment, we're so caught up in building and shipping that we forget to share about the amazing launches and products we've helped shape, so here are a few we love
AI
- Helped Motion build agentic workflows + data and inference infrastructure to scale LLM “expert” performance
- Migrated core messaging for ClassDojo to a hybrid RN architecture with AI‑scale reliability practices and automated pipelines that cleared accessibility mandates
- Built Extropic's distributed computing platform for high‑compute AI workloads with fine‑grained job scaling
- Creating an AI powered routing algorithm for GoBolt
- Prototyped an AI Hub with Retool for enterprise teams, with agent‑ready data prep and admin workflows to operationalize LLM use cases
COMMERCE
- Helped Shopify across a number of engineering initiatives
- Lead and support SKIMS in engineering across frontend, storefront, POS, and backend infrastructure
- Migrated October's Very Own from a custom Hydrogen frontend to Liquid and continue to level up their storefront
- Migrated Gibson to Shopify across DTC, B2B, POS, and international markets
- Accelerating Athletic Greens across numerous full-stack and AI engineering initiatives
- Integrated Vantage's AI-powered search into Shopify (later acquired by Shopify)
- Migrated Firstleaf from Solidus to Shopify while building custom subscription and state-by-state alcohol compliance tooling
- Leading engineering for Mattel's American Girl
- Led Knix through numerous frontend initiative's
- Developed and launched a Shop Mini enabling Shop customers to virtual try-on apparel from their fav stores
CRYPTO
- Helped Coinbase across the stack to build and launch their new Base app
- Designed core Berachain launch pages and helped build the infrastructure for their asset and NFT bridges
- Initialized the work on Alchemy’s React Native wallet SDK and beefed up their SDK for web
- Helped Magic deliver their Newton platform for agentic, autonomous finance
- Helping XMTP build the next phase of their path to messaging world domination
- Built Dynamic’s Swift SDK for wallet integration on native iOS
- Helped Courtyard migrate critical marketplace functionality away from third-party vendors building an in-house indexer and relayer
CONSUMER
- Re‑architected CookUnity's React Native app and CI
- Designed flight planner features for Pivotal Aero
- Built a performant, configurable content system for Stanford's Hoover Institution to host AI‑generated narratives and rich media with an admin‑driven component library
- Helped Wakefern with UI/UX needs for their ShopRite app
HEALTHCARE
- Provisioned the data platform and ML infrastructure for ICP’s intelligent diabetes care solution.
- Refactored Sully's audio recording capabilities and built AI diarization for their medical scribe extension and app
FINTECH
- Built a Stripe-integrated risk engine for Canonical to automate fraud detection workflows
We also helped…
Kraken, General Catalyst, OpenSea, Bombas, Todd Snyder, Axiom, VF Corporation, Ava Labs, ButcherBox, DIMO, Argo, Titles, Humi, Ardene, Seed, Delphi, Zuma, ShopHQ, Segen, PawProsper, TYR, EQL, RacquetGuys, Heron Finance, Mantle, and many more
Can't wait for 2026 and building more of the world's best products with amazing founders ⚡️