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Lightning Labs Launches Wavelength: “Bitcoin on Easy Mode” for Developers and Autonomous Agents

✇Bitcoin Magazine
By: Juan Galt

Bitcoin Magazine

Lightning Labs Launches Wavelength: “Bitcoin on Easy Mode” for Developers and Autonomous Agents

Lightning Labs, the company developing core Lightning Network software including Lnd, Loop, and Taproot Assets, has released the alpha version of Wavelength. The toolkit enables developers and AI agents to add self-custodial Bitcoin payments to applications through a simple non-custodial API, without running nodes, managing channels, or sourcing liquidity.

In a July 21, 2026 blog post, Lightning Labs described Wavelength as “Bitcoin on Easy Mode for Agents and Humans.” The company stated that the Lightning Network already delivers instant, global, low-fee payments under user control, but previously required infrastructure most builders preferred not to operate. Wavelength closes that gap by turning the hard parts of Bitcoin and Lightning integration into a handful of API calls.

High-Level Overview

Wavelength embeds a self-custodial wallet that runs inside web or mobile apps (via WebAssembly or compiled binaries) or as a standalone client. Users control their own keys on-device. The system supports on-chain Bitcoin, Lightning payments via atomic swaps, and an Ark-like settlement layer for fast, low-cost off-chain transfers that can settle in batches to the blockchain. Every off-chain payment uses a standard BOLT 11 invoice, so the wallet interoperates with the existing Lightning Network from the first integration.

Lightning payments route through Loop for deep, reliable liquidity. A coordination service settles transfers between users but never takes unilateral control of funds. According to the announcement, users can always perform a unilateral exit to on-chain Bitcoin at any time via an explicit exit command, without needing cooperation, the Wavelength SDK is open source.

The same Wavelength API is exposed to AI agents as typed tool calls through the Model Context Protocol (MCP). Agents can hold balances and pay for API calls, data feeds, or other agent services in fractions of a cent. Wallet creation and unlocking designed to remain outside the agent channel so seeds and passwords are not exposed to the model. This pairs with L402, Lightning Labs’ protocol for machine-native authentication and per-request Lightning payments.

Core commands cover the full lifecycle: create/unlock, balance, recv (for addresses or invoices), send, activity, and exit. Integration options include the embedded SDK, a gRPC/REST API, browser WASM package, and an MCP server. Documentation is structured for both human developers and agents, including llms.txt indexes and agent onboarding guidance.

Availability and Roadmap

Wavelength is available immediately on Signet and testnet. Mainnet access is invitation-only; interested parties can request it after installing the toolkit. Bitcoin is supported at launch. Stablecoin support is planned via Taproot Assets so the same API surface can handle both. Future work includes deeper mobile embedding and optional direct Lightning channel support using Lnd.

Lightning Labs noted in its announcement that during the closed alpha, Lightning transactions carry a minimal 1 basis point service fee (plus standard network routing fees), with ordinary Bitcoin network fees applying for on-chain activity. Pricing may evolve.

On X, Lightning Labs summarized the release: “Announcing Wavelength, the easiest way to integrate bitcoin for agents and humans. With a simple non-custodial API, anyone can integrate Lightning into their app and get instant, high volume, low fee transactions. Machines can pay machines. Humans can pay humans. Anywhere.” A follow-up post directed builders to a form for early mainnet access.

The release positions Wavelength as infrastructure that lowers the barrier for application developers, “vibe coders,” and autonomous agents to offer self-custodial Bitcoin payments by default rather than as a specialist feature. Full documentation, quickstarts, and the open-source repository are available at wavelength.lightning.engineering and the linked GitHub project.

This post Lightning Labs Launches Wavelength: “Bitcoin on Easy Mode” for Developers and Autonomous Agents first appeared on Bitcoin Magazine and is written by Juan Galt.

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New Markdown rival: Open-source DGML format aims to turn docs into data that AI (and humans) can trust

✇GeekWire
By: Todd Bishop
L-R: Mantra CEO John Patrick Mullin, Docugami CEO Jean Paoli, and Inveniam CEO Patrick O’Meara. The companies are partnering to make DGML a standard for AI, with Docugami turning documents into data, Inveniam verifying it on a blockchain, and Mantra providing the chain.

Jean Paoli has spent his career making documents readable by machines — first as a co-creator of XML, then helping build the file formats behind Microsoft Office. Now his Kirkland, Wash.-based startup, Docugami, is open-sourcing the technology at the heart of its business, betting it can become a standard way to turn documents into data that people and AI agents can trust. 

The company is releasing its technology, called DGML (short for Document Graph Markup Language), under Apache 2.0, a widely used open-source license, so other developers and companies can adopt it.

The idea is to turn it into a shared standard that no single company owns, much as XML became a common foundation across the tech industry. 

The move reflects a shift in where the value is created in AI. Docugami until now has made its money selling software that turns unstructured documents into usable data. It’s betting now that there’s more value in proving that data is trustworthy instead. 

How it works: Docugami is teaming up with Inveniam, a Detroit company whose software helps big investors keep tabs on the mountains of paperwork behind real estate and other hard-to-value assets. Inveniam will record a kind of digital fingerprint of each piece of DGML data on NVNM Chain, its blockchain built with Mantra, a crypto firm that Inveniam is acquiring.

That means, for example, that a single fact buried in a 200-page lease — such as the rental rate, a renewal option, or a default clause — can be verified on its own, without exposing the whole document. An investor, auditor, or AI agent can trace it to the page it came from. 

To work with documents, AI systems usually convert them into a simpler format first. DGML enters a growing field of contenders in that regard, competing with the popular Markdown format and DocLang, a new open standard for AI-ready documents backed by IBM, Nvidia and Red Hat.

The business model: This is a big move for a company of Docugami’s size, taking the 30-person startup in a new direction. Paoli is handing the industry the technology his team spent years building, and pinning the company’s future on a larger idea.

The plan is to make money not from the format itself but from the value of the trusted data. Once a company converts its leases or loans into DGML and anchors the key numbers on the blockchain, investors, lenders and auditors can pay to draw on that verified data.

Docugami will share in the revenue through its partnership with Inveniam. The company also stands to collect a small fee each time a piece of data is recorded on the chain. 

The company is giving away the DGML format and a working version of the software, but not everything. Paoli said the company is keeping some of its own technology private, including AI models it has fine-tuned to read documents, and could sell those or other tools to enterprises. 

“The business model of everybody is changing. And if you know any company where it’s not true, you need to tell me, because I haven’t met them yet,” Paoli said in an interview. 

Docugami has raised about $13 million to date, including a $10 million seed round in 2020 that drew the first investment in Grammarly’s history.

The partnership: Paoli met Patrick O’Meara, Inveniam’s CEO, a few months ago, through a former Microsoft colleague who had become one of O’Meara’s advisers. They quickly realized they had been working toward the same idea from different directions.

Inveniam, founded in 2017, helps big investors keep track of assets that are hard to value, like office towers, private loans and infrastructure. It monitors the documents behind those assets and flags changes as they happen, and its clients include some of the world’s largest sovereign wealth funds, according to O’Meara.

What it lacked was a consistent way to break those documents into verifiable pieces. That is what Docugami provides.

“We’re not putting the data itself on-chain, just a fingerprint of the document. Change one bit, one byte, one pixel, and the hash won’t match,” O’Meara said.

The blockchain comes from Mantra, a crypto company run by John Patrick Mullin. Inveniam invested $20 million in Mantra last year and has since agreed to acquire it outright. Mantra’s OM token collapsed in April 2025, erasing several billion dollars in value. 

Paoli said the project uses the underlying blockchain, not the token.

“Crypto as an industry has gone through a lot of changes in the last 18 to 24 months, and it’s growing up in a lot of ways. This is a real use case with fundamental value, not just pure speculation,” Mantra’s Mullin said in an interview. 

The result is a division of labor: Docugami turns documents into data, Inveniam verifies it and brings the customers, and Mantra provides the chain where the proof is recorded.

The DGML specification, sample documents and reference code are at dgml.io and on GitHub. 

Editor’s note: This story was updated after publication to correct the name of a competing document format, DocLang, and to note that Inveniam’s blockchain is called NVNM Chain.

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Beyond grep: The case for a context-rich AI coding harness

✇Ars Technica
By: Samuel Axon

There are a lot of AI coding applications out there, and as impressive as large language models and the agents they enable have become, many of the most recent developments in AI-assisted development have been in the software that manages those models, not just the models themselves.

Earlier this summer, I spoke with the head of product for Claude Code, Anthropic's Cat Wu, about that company's approach to building that software.

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The code AI forgot: logcat.ai raises $2.55M to put agents to work on device operating systems

✇GeekWire
By: Todd Bishop
Varun Chitre, CEO, left, and Tarun Vashisth, CTO, co-founders of logcat.ai. (logcat.ai Photos)

The past two years have transformed the world of software development, but there’s at least one area that remains largely untouched by artificial intelligence: the operating-system layer inside phones, vehicles, and other connected devices. 

A Seattle startup called logcat.ai has raised $2.55 million to change that.

Co-founded by CEO Varun Chitre and CTO Tarun Vashisth, two engineers with years of experience building device software, logcat.ai is developing a system of AI agents that autonomously hunt down bugs across the kernel, modem, and firmware of devices running Android or Linux.

The pre-seed round was led by Founders’ Co-op, with participation from Act One Ventures, TheFounderVC, Shorewind Capital, Clayoquot Capital, and Alumni Ventures. 

“It’s one of the toughest areas of software engineering, and it doesn’t get a lot of exposure. Operating-system engineering is virtually hidden today,” Chitre said in an interview.

It’s also a challenge for many companies given a shortage of engineers who specialize in the field, compared to the much larger population of developers who build apps and software that run on top of the operating system.

How it works: An engineer using logcat.ai uploads the log files a device generates when something goes wrong — such as bug reports and kernel logs — and logcat.ai’s software analyzes them together to find the root cause and point to where in the code to fix it. Each finding cites the exact log line it came from, so an engineer can check the work.

Currently, logcat.ai finds the root cause and recommends a fix. The larger plan is to have the AI write the fixes, test them, and eventually build new features on its own, with engineers approving the work before it’s deployed.

The long-term goal, Chitre said, is to become the standard tool for building and maintaining operating systems on new and existing hardware — from smartphones to cars to robots and other embedded systems — so a company can ship without a full-stack specialist on staff.

“We’re moving toward a world where software and intelligence extend far beyond our laptops and phones, yet the tooling to build high-quality products for that world is still missing,” said Aviel Ginzburg, general partner at Founders’ Co-op, in a statement.

He called Chitre and Vashisth “one of the only teams in the world truly up for the challenge.”

Traction: The company says it has served hundreds of engineering teams in a public beta, analyzed more than 10 billion lines of trace data, and run thousands of automated investigations. It’s generating revenue but isn’t ready to disclose numbers or customers. 

Competitive landscape: Chitre said logcat.ai’s main competition isn’t another product but in-house scripts and the knowledge locked in a few senior engineers’ heads. App-level crash tools like Google’s Crashlytics and Sentry stop at the app layer and don’t do the deeper system debugging.

Specialist vendors and the contract manufacturers that build devices are potential partners more than rivals, Chitre said, since they face the same engineer shortage.

GeekWire first reported on logcat.ai in March, in a Startup Radar roundup.

The team: Chitre and Vashisth met at Esper, the Bellevue, Wash.-based device-management company, where they worked together for more than seven years. They started logcat.ai because they had spent years doing debugging by hand and knew what was missing.

Chitre has spent more than 13 years in the field, getting operating systems to boot and run on new hardware and porting new Android releases and Linux kernels onto older devices. He was also a maintainer of LineageOS, a widely used open-source version of Android. 

Vashisth has led engineering teams working across Android, Linux, and iOS, and brings a background in large-scale distributed systems. At Esper, he rose to senior software engineering manager. His prior experience includes platform-architecture engineering at Target.

For now, the company is just the two founders: Chitre in the Seattle area, Vashisth in Bengaluru, India. They plan to hire about 10 people over the next year, with a distributed team working remotely from wherever they can find the specialized talent.

They know those hires won’t be easy to find, given the scarcity of people in the field. “That’s the same shortage our product exists to address,” Chitre said, “and we’re not exempt from it.” 

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