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Apple has announced it will release an update for specific iPhones today — and announcing this in advance is pretty much unprecedented.
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And from its name through to its CEO and base of operations, it seems Sony product fans have much to feel relieved about.
Slack today announced more than 30 new capabilities for Slackbot, its AI-powered personal agent, in what amounts to the most sweeping overhaul of the workplace messaging platform since Salesforce acquired it for $27.7 billion in 2021. The update transforms Slackbot from a simple conversational assistant into a full-spectrum enterprise agent that can take meeting notes across any video provider, operate outside the Slack application on users’ desktops, execute tasks through third-party tools via the Model Context Protocol (MCP), and even serve as a lightweight CRM for small businesses — all without requiring users to install anything new.
The announcement, timed to a keynote event that Salesforce CEO Marc Benioff is headlining Tuesday morning, arrives less than three months after Slackbot first became generally available on January 13 to Business+ and Enterprise+ subscribers. In that short window, Slack says the feature is on track to become the fastest-adopted product in Salesforce’s 27-year history, with some employees at customer organizations reporting they save up to 90 minutes per day. Inside Salesforce itself, teams claim savings of up to 20 hours per week, translating to more than $6.4 million in estimated productivity value.
“Slackbot is smart. It’s pleasant, and I think it’s endlessly useful,” Rob Seaman, Slack’s interim CEO and former chief product officer, told VentureBeat in an exclusive interview ahead of the announcement. “The upper bound of use cases is effectively limitless for it.”
The release signals Slack’s clearest bid yet to become what Seaman and the company’s leadership describe as an “agentic operating system” — a single surface through which workers interact with AI agents, enterprise applications, and one another. It also marks a direct challenge to Microsoft, which has spent the past two years embedding its Copilot assistant across the entirety of its productivity stack.
The features announced Tuesday organize around several major capability areas, each designed to push Slackbot well beyond the role of a chatbot and into something closer to an autonomous digital coworker.
The most foundational may be what Slack is calling AI-Skills — reusable instruction sets that define the inputs, the steps, and the exact output format for a given task. Any team can build a skill once and deploy it on demand. Slackbot ships with a built-in library for common workflows, but users can also create their own. Critically, Slackbot can recognize when a user’s prompt matches an existing skill and apply it automatically, without being explicitly told to do so. “Think of these as topics or instructions — basically instructions for Slackbot to perform a repeat task that the user might want to do, that they can share with others, or a company might be able to set up for their whole company,” Seaman explained.
Deep research mode gives Slackbot the ability to conduct extended, multi-step investigations that take approximately four minutes to complete — a significant departure from the instant-response paradigm of most enterprise chatbots. Slack chose not to demonstrate this feature on stage at the keynote, Seaman said, precisely because its value lies in depth, not speed. MCP client integration, meanwhile, allows Slackbot to make tool calls into external systems through the Model Context Protocol, meaning it can now create Google Slides, draft Google Docs, and interact with the more than 2,600 apps in the Slack Marketplace and the 6,000-plus apps built over two decades for the Salesforce AppExchange. “We’re going all in on MCP for Slackbot,” Seaman said. “MCP clients and MCP servers are becoming very mature.”
Meeting intelligence allows Slackbot to listen to any meeting — not just Slack huddles, but calls on Zoom, Google Meet, or any other provider — by tapping into the user’s local audio through the desktop application. It captures discussions, summarizes decisions, surfaces action items, and because Slackbot is natively connected to Salesforce, it can log actions and update opportunities directly in the CRM. Slackbot on Desktop extends the agent outside the Slack container entirely, while voice mode adds text-to-speech and speech-to-text capabilities, with full speech-to-speech functionality under active development.
Slackbot is built on Anthropic’s Claude model, a detail Seaman confirmed ahead of the keynote, where Anthropic’s leadership will appear alongside Slack executives on stage. The partnership underscores the deepening relationship between the two companies: Anthropic’s technology powers the reasoning layer, while Slack’s “context engineering” — the process of determining exactly which information from a user’s channels, files, and messages should be fed into the model’s context window — determines the quality and relevance of every response.
Managing the cost of that reasoning at enterprise scale is one of the most significant technical and financial challenges the team faces. Slackbot is included in Business+ and Enterprise+ plans at no additional consumption charge — a deliberate strategic choice that places the burden of cost optimization squarely on Slack’s engineering team rather than on customers.
“A lot of what we’ve done is in the context engineering phase, working really closely with Anthropic to make sure that we’re optimizing the RAG phase, optimizing our system prompts and everything, to make sure we’re getting the right amount of context into the context window and not obviously making fiscally irresponsible decisions for ourselves,” Seaman said. Starting in April, Slackbot will also become available in a limited sampling capacity to users on Slack’s free and Pro plans — a move designed to drive conversion up the pricing tiers.
The extension of Slackbot beyond the Slack application window — particularly its ability to listen to meetings and view screen content — raises immediate questions about employee surveillance, especially in large enterprise environments where tens of thousands of workers may be subject to company-wide IT policies.
Seaman was emphatic that every capability is user-initiated and opt-in. Slackbot cannot listen to audio unless the user explicitly tells it to take meeting notes. It cannot view the desktop autonomously; in its current form, users must manually capture and share screenshots. And it inherits every permission the organization has already established in Slack.
“Everything is user opt-in. That’s a key tenet of Slack,” Seaman said. “It’s not rogue looking at your desktop or autonomously looking at your desktop. It’s very important to us, and very important to our enterprise customers.” On Slackbot’s memory feature — which allows it to learn user preferences and habits over time — Seaman said the company has no plans to make that data available to administrators. Users can flush their stored preferences at any time simply by telling Slackbot to do so.
Among the most important features in Tuesday’s release is a native CRM built directly into Slack, targeting small businesses that haven’t yet adopted a dedicated customer relationship management system.
The logic is straightforward: small companies typically adopt Slack early in their lifecycle, often on the free tier, and their customer conversations already happen in channels and direct messages. Slack’s native CRM reads those channels, understands the conversations, and automatically keeps deals, contacts, and call notes up to date. When companies are ready to scale, every record is already connected to Salesforce — no migrations, no starting over.
“The hypothesis is that along the way, companies are effectively going to have moments where a CRM might matter,” Seaman said. “Our goal is to make it available to them as a default, so as they are starting their company and their company is growing, it’s just right there for them. They don’t have to think about going off and procuring another tool.”
The feature also represents a response to a growing competitive threat. As the Wall Street Journal reported earlier this year, a wave of startups and individual developers have begun “vibe coding” their own lightweight CRMs, emboldened by the capabilities of large language models. By embedding CRM directly into Slack — the tool many of those same startups already depend on — Salesforce aims to make the procurement of a separate system unnecessary.
The announcements arrive at a moment of intense competitive pressure. Microsoft has integrated Copilot across its entire productivity suite, giving it a distribution advantage that reaches into virtually every Fortune 500 company. Google has been similarly aggressive with Gemini across Workspace. And standalone AI tools from OpenAI to Anthropic threaten to fragment the enterprise AI experience.
Seaman took a measured approach when asked directly about competitive positioning, invoking a mantra he said Slack uses internally: “We are competitor aware, but customer obsessed.”
“I think there are two things that really stand out. One, we have a context advantage — if you look at the way people use Slack, they love it. They use it so much, constantly communicating with their colleagues, openly thinking, working in public project channels. Two is the user experience. We focus so much on how our product feels in people’s hands.”
That context advantage is real but not guaranteed. Slack’s strength lies in the richness and volume of conversational data flowing through its channels — data that, when fed into an AI model, can produce responses with a degree of organizational awareness that competitors struggle to match. But Microsoft’s Teams captures similar conversational data, and its deep integration with Windows, Office, and Azure gives it a systems-level advantage that Slack, operating as a single application, cannot easily replicate.
Starting this summer, every new Salesforce customer will receive Slack automatically provisioned and AI-powered from day one — a bundling play that ensures the messaging platform reaches the broadest possible enterprise audience. Salesforce reported $41.5 billion in revenue for fiscal year 2026, up 10% year-over-year, with Agentforce ARR reaching $800 million. But Wall Street has remained skeptical about whether AI will ultimately erode demand for traditional enterprise software, and Salesforce’s stock has underperformed the broader Nasdaq over the past year. More Slack users in more organizations gives AI-driven features more surface area to prove their value.
Tuesday’s launch is the first major product release under Seaman’s leadership. He assumed the interim CEO role after former Slack CEO Denise Dresser departed in December 2025 to become OpenAI’s first chief revenue officer — a move that signaled even Salesforce’s own executives felt the gravitational pull of frontier AI companies. The overarching thesis embedded in the announcement — that Slack is evolving from a messaging platform into an operating system for AI agents — is as risky as it is ambitious.
“One of the fundamental tenets of an operating system is that it obscures the complexity of the hardware from the end user,” Seaman said. “There are thousands of apps and agents out there, and that can be overwhelming. I think that’s our job — to be the OS that obscures that complexity, so you just use it like it’s a communication tool.”
When asked whether Slack risks losing its simplicity by trying to do everything, Seaman didn’t flinch. “There’s absolutely a risk,” he said. “That’s what keeps us up at night.”
It’s a remarkably candid admission from the leader of a platform that just launched 30 new features in a single day. The company that won the hearts of millions of workers with playful emoji reactions and frictionless messaging is now betting its future on meeting transcription, CRM pipelines, desktop agents, and enterprise orchestration. Whether Slack can absorb all of that ambition without losing the thing that made people love it in the first place isn’t just a product question — it’s the $27.7 billion question that Salesforce is still trying to answer.
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Anthropic appears to have accidentally revealed the inner workings of one of its most popular and lucrative AI products, the agentic AI harness Claude Code, to the public.
A 59.8 MB JavaScript source map file (.map), intended for internal debugging, was inadvertently included in version 2.1.88 of the @anthropic-ai/claude-code package on the public npm registry pushed live earlier this morning.
By 4:23 am ET, Chaofan Shou (@Fried_rice), an intern at Solayer Labs, broadcasted the discovery on X (formerly Twitter). The post, which included a direct download link to a hosted archive, acted as a digital flare. Within hours, the ~512,000-line TypeScript codebase was mirrored across GitHub and analyzed by thousands of developers.
For Anthropic, a company currently riding a meteoric rise with a reported $19 billion annualized revenue run-rate as of March 2026, the leak is more than a security lapse; it is a strategic hemorrhage of intellectual property.The timing is particularly critical given the commercial velocity of the product.
Market data indicates that Claude Code alone has achieved an annualized recurring revenue (ARR) of $2.5 billion, a figure that has more than doubled since the beginning of the year.
With enterprise adoption accounting for 80% of its revenue, the leak provides competitors—from established giants to nimble rivals like Cursor—a literal blueprint for how to build a high-agency, reliable, and commercially viable AI agent.
We’ve reached out to Anthropic for an official statement on the leak and will update when we hear back.
The most significant takeaway for competitors lies in how Anthropic solved “context entropy”—the tendency for AI agents to become confused or hallucinatory as long-running sessions grow in complexity.
The leaked source reveals a sophisticated, three-layer memory architecture that moves away from traditional “store-everything” retrieval.
As analyzed by developers like @himanshustwts, the architecture utilizes a “Self-Healing Memory” system.
At its core is MEMORY.md, a lightweight index of pointers (~150 characters per line) that is perpetually loaded into the context. This index does not store data; it stores locations.
Actual project knowledge is distributed across “topic files” fetched on-demand, while raw transcripts are never fully read back into the context, but merely “grep’d” for specific identifiers.
This “Strict Write Discipline”—where the agent must update its index only after a successful file write—prevents the model from polluting its context with failed attempts.
For competitors, the “blueprint” is clear: build a skeptical memory. The code confirms that Anthropic’s agents are instructed to treat their own memory as a “hint,” requiring the model to verify facts against the actual codebase before proceeding.
The leak also pulls back the curtain on “KAIROS,” the Ancient Greek concept of “at the right time,” a feature flag mentioned over 150 times in the source. KAIROS represents a fundamental shift in user experience: an autonomous daemon mode.
While current AI tools are largely reactive, KAIROS allows Claude Code to operate as an always-on background agent. It handles background sessions and employs a process called autoDream.
In this mode, the agent performs “memory consolidation” while the user is idle. The autoDream logic merges disparate observations, removes logical contradictions, and converts vague insights into absolute facts.
This background maintenance ensures that when the user returns, the agent’s context is clean and highly relevant.
The implementation of a forked subagent to run these tasks reveals a mature engineering approach to preventing the main agent’s “train of thought” from being corrupted by its own maintenance routines.
The source code provides a rare look at Anthropic’s internal model roadmap and the struggles of frontier development.
The leak confirms that Capybara is the internal codename for a Claude 4.6 variant, with Fennec mapping to Opus 4.6 and the unreleased Numbat still in testing.
Internal comments reveal that Anthropic is already iterating on Capybara v8, yet the model still faces significant hurdles. The code notes a 29-30% false claims rate in v8, an actual regression compared to the 16.7% rate seen in v4.
Developers also noted an “assertiveness counterweight” designed to prevent the model from becoming too aggressive in its refactors.
For competitors, these metrics are invaluable; they provide a benchmark of the “ceiling” for current agentic performance and highlight the specific weaknesses (over-commenting, false claims) that Anthropic is still struggling to solve.
Perhaps the most discussed technical detail is the “Undercover Mode.” This feature reveals that Anthropic uses Claude Code for “stealth” contributions to public open-source repositories.
The system prompt discovered in the leak explicitly warns the model: “You are operating UNDERCOVER… Your commit messages… MUST NOT contain ANY Anthropic-internal information. Do not blow your cover.”
While Anthropic may use this for internal “dog-fooding,” it provides a technical framework for any organization wishing to use AI agents for public-facing work without disclosure.
The logic ensures that no model names (like “Tengu” or “Capybara”) or AI attributions leak into public git logs—a capability that enterprise competitors will likely view as a mandatory feature for their own corporate clients who value anonymity in AI-assisted development.
The “blueprint” is now out, and it reveals that Claude Code is not just a wrapper around a Large Language Model, but a complex, multi-threaded operating system for software engineering.
Even the hidden “Buddy” system—a Tamagotchi-style terminal pet with stats like CHAOS and SNARK—shows that Anthropic is building “personality” into the product to increase user stickiness.
For the wider AI market, the leak effectively levels the playing field for agentic orchestration.
Competitors can now study Anthropic’s 2,500+ lines of bash validation logic and its tiered memory structures to build “Claude-like” agents with a fraction of the R&D budget.
As the “Capybara” has left the lab, the race to build the next generation of autonomous agents has just received an unplanned, $2.5 billion boost in collective intelligence.
While the source code leak itself is a major blow to Anthropic’s intellectual property, it poses a specific, heightened security risk for you as a user.
By exposing the “blueprints” of Claude Code, Anthropic has handed a roadmap to researchers and bad actors who are now actively looking for ways to bypass security guardrails and permission prompts.
Because the leak revealed the exact orchestration logic for Hooks and MCP servers, attackers can now design malicious repositories specifically tailored to “trick” Claude Code into running background commands or exfiltrating data before you ever see a trust prompt.
The most immediate danger, however, is a concurrent, separate supply-chain attack on the axios npm package, which occurred hours before the leak.
If you installed or updated Claude Code via npm on March 31, 2026, between 00:21 and 03:29 UTC, you may have inadvertently pulled in a malicious version of axios (1.14.1 or 0.30.4) that contains a Remote Access Trojan (RAT). You should immediately search your project lockfiles (package-lock.json, yarn.lock, or bun.lockb) for these specific versions or the dependency plain-crypto-js. If found, treat the host machine as fully compromised, rotate all secrets, and perform a clean OS reinstallation.
To mitigate future risks, you should migrate away from the npm-based installation entirely. Anthropic has designated the Native Installer (curl -fsSL https://claude.ai/install.sh | bash) as the recommended method because it uses a standalone binary that does not rely on the volatile npm dependency chain.
The native version also supports background auto-updates, ensuring you receive security patches (likely version 2.1.89 or higher) the moment they are released. If you must remain on npm, ensure you have uninstalled the leaked version 2.1.88 and pinned your installation to a verified safe version like 2.1.86.
Finally, adopt a zero trust posture when using Claude Code in unfamiliar environments. Avoid running the agent inside freshly cloned or untrusted repositories until you have manually inspected the .claude/config.json and any custom hooks.
As a defense-in-depth measure, rotate your Anthropic API keys via the developer console and monitor your usage for any anomalies. While your cloud-stored data remains secure, the vulnerability of your local environment has increased now that the agent’s internal defenses are public knowledge; staying on the official, native-installed update track is your best defense.