In Today’s edition:

1. Anthropic Introduces Claude Fable and Mythos 5.1

2. PMNA TUTORIAL: How to Use Claude to Rescue a Messy Monday Board You Just Inherited

3. Slack Introduces a New Way to Bring AI Agents Into Your Workspace

4. TOOL REVIEW: Dial Helps AI Agents Have Their Own Phone Numbers

5. Exciting Career Opportunities for Product and Project Management Professionals

Reading time: 5 minutes

HEADLINE NEWS

Anthropic Introduces Claude Fable and Mythos 5.1

Anthropic has introduced Claude Fable 5.1 and Claude Mythos 5.1, a new generation of models aimed at pushing the boundaries of coding, knowledge work, and advanced research. The two models are essentially the same underlying system, but differ in their safeguards and availability.

• Fable 5.1 is generally available, while Mythos 5.1 is restricted to trusted-access programs for higher-risk work such as cybersecurity and life sciences.

• The models are designed for advanced coding and knowledge work, with Anthropic positioning them among its most capable systems yet.

• Mythos 5.1 uses stronger safeguards because its capabilities can be applied to sensitive scientific and cybersecurity tasks.

• Anthropic is addressing customer concerns around price and data retention, making changes intended to make the models more practical for businesses.

• The release highlights AI's growing role in scientific research, with Anthropic suggesting these models offer an early glimpse of how AI could contribute to scientific progress.

The Fable 5.1 and Mythos 5.1 launch is less about simply making Claude smarter and more about making increasingly powerful AI usable in real-world, high-stakes environments. Anthropic is pairing stronger capabilities with different levels of safeguards, signaling that the next phase of frontier AI will be defined as much by how safely it can be deployed as by how intelligent it becomes. Read More 

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PMNA TUTORIAL: How to Use Claude to Find the Work Your Monday Board Is Hiding

Your monday board records the work someone remembered to type in, in the fields they bothered to fill. The gap between that and the actual work is hidden work, and it comes in three forms: items no view shows you, items the columns describe incorrectly, and work that only exists in an update thread. Monday's filters catch the first form and cannot catch the other two, because a filter needs a predicate and a status column contradicting a comment is not one.

Key Steps and Ideas:

1. Connect the monday MCP connector, confirm your board access, and set an explicit read-only posture for the session.

2. Map the board with get_board_info to pull column IDs, column types, status label indexes and hierarchy_type before writing a single filter.

3. Define what a fully described item looks like on this board, and state that standard to Claude in one sentence.

4. Sweep for structural gaps: empty owner, empty due date, empty status, and overdue items still in an active status.

5. Find stale work by comparing updated_at against a 30-day cutoff, or by inverting get_board_activity against the full item list.

6. Cross-reference the sweep results down to the items that carry real exposure, such as no owner plus a due date inside 14 days.

7. Pull update threads and item descriptions with get_updates, scoped to a window your context can hold.

8. Run the contradiction pass against a fixed three-category taxonomy, requiring an item ID and the exact triggering phrase for every finding.

9. Spot-check three or four findings in monday itself before treating any of them as fact.

10. Rank the findings on dated exposure, visibility and blocked people, and cap the output at ten rows.

11. Write the results back as a dated doc and a saved "Needs Attention" view that catches the structural gaps permanently.

12. Re-run the sweep monthly and the read pass quarterly, or before any meeting where you have to defend a status.

What You Should Consider:

The method works because it splits the job correctly: deterministic queries handle the missing fields, and the language model handles the mismatches between what a column says and what a person wrote. Its failure mode is confident false positives, which is why every finding carries a quoted phrase and why the verification step is not optional. Scope it honestly, note that a 60-day sample is not an audit, and check where your organisation stands on reading colleagues' update threads before you pull them into a chat.

FULL GUIDE HERE

Slack Introduces a New Way to Bring AI Agents Into Your Workspace

Slack has launched Add to Slack, a new way for teams to deploy AI agents directly into their Slack workspace without building custom integrations. The feature connects Slack with leading AI and agent-building platforms, making it possible for both technical and non-technical users to bring custom agents into everyday team workflows.

• One-click deployment: Users can build an agent on supported platforms and add it to Slack without custom code, complex webhooks, or technical setup.

• 10 launch partners: Add to Slack launches with platforms including n8n, OpenAI, LangChain, Lovable, Vercel, Hyperagent, and Superhuman.

• Agents work inside channels: Once deployed, agents can interact with teammates, receive tasks, and collaborate directly within Slack conversations.

• Enterprise controls built in: Agents automatically inherit Slack workspace permissions and data boundaries, giving IT teams centralized governance and visibility.

• Built for everyday business tasks: Teams can use these agents for everything from HR onboarding and sales research to support, engineering, campaign reporting, and workflow automation.

With Add to Slack, Slack is positioning itself as more than a place where employees communicate; it wants to become the workspace where people and AI agents work together. By removing much of the technical friction involved in deploying agents while keeping enterprise governance intact, Slack could make agent adoption considerably easier for businesses. Read More 

TOOL REVIEW: Dial Helps AI Agents Have Their Own Phone Numbers

Dial is a communications infrastructure platform built specifically for AI agents, giving them a real phone identity they can use to communicate with people through voice calls, SMS, WhatsApp, and inbound messages. Rather than being only a voice-agent builder, Dial provides the underlying phone numbers, APIs, SDKs, MCP connectivity, and event infrastructure that allow AI agents to interact with people through traditional communication channels.

• AI Voice Calls — AI agents can make and receive real-time phone calls, follow custom instructions, converse naturally, and produce transcripts and call outcomes for downstream workflows.

• Two-Way Messaging — Agents can send and receive SMS and WhatsApp messages, enabling automated follow-ups, notifications, customer conversations, and other messaging-based workflows.

• Programmable Phone Numbers — Each Dial number can serve as an agent's communication identity, with configurable inbound instructions that determine how its AI voice agent handles incoming calls.

• Developer and Agent Integrations — Dial can be accessed through a REST API, MCP server, CLI, Node/Python SDKs, and integrations with frameworks such as LangChain, CrewAI, Vercel AI SDK, AutoGen, and Microsoft's Agent Framework.

• Event-Driven Automation — Dial exposes events such as incoming messages, call completions, and message-status changes, allowing external systems and workflows to react automatically instead of continually polling for updates.

For project managers, Dial is particularly relevant because it can turn AI agents into active participants in operational workflows rather than limiting them to chat interfaces. Teams could use it to automate appointment confirmations, customer follow-ups, inbound support, lead qualification, notifications, and escalation to human staff, while connecting those interactions to existing applications and automation systems.

TOOL LINK

Exciting Career Opportunities for Product and Project Management Professionals

• Engineering Program Manager @ Cisco

  📍 Research Triangle Park, NC.

• XP Product Manager @ Siemens Healthineers

  📍 Cary, NC.

• Director, AI Strategy and Use Case Management @ Ally Financial

  📍 Charlotte, NC.

• Segment Product Manager @ Lenovo

  📍Morrisville, NC.

• Associate Project Manager @ RoviSys

  📍 Holly Springs, NC.

THAT’S A WRAP