When the task is 'research this market and write a 3-page summary' or 'find me 50 leads in this niche with verified contact info', a chat LLM is the wrong tool — too much hand-holding. Manus runs the multi-step plan autonomously and returns a deliverable. Claude is the QA gate before you trust it.
Plans, executes, and returns a deliverable for high-context multi-step work. Good for research, list building, lightweight analysis. The right pick when the task would otherwise be a dozen chat turns and a human aggregator.
Read the Manus output critically. Cross-check 3 to 5 claims with citations. Don't act on autonomous output without a human-in-the-loop pass.
Fast mode for Claude Opus 4.8 is being deprecated and will be removed on November 30, 2026. Developers using fast mode with Opus 4.8 must migrate to Claude Opus 5.5 to continue using the feature.
Anthropic released the Usage and Cost Analytics API in beta, enabling organizations to monitor token usage and costs across Claude API consumption with filtering and grouping by dimensions like model, workspace, and API key. The API provides /v1/organizations/analytics/usage and /v1/organizations/analytics/cost endpoints. Legacy usage reporting endpoints remain supported.
The maximum request size for the Messages API and Token Counting API doubled from 32 MB to 128 MB on the first-party Claude API, allowing developers to process significantly larger payloads in single requests. This change takes effect immediately and applies to both APIs.
The Claude Tag API is now available in beta for Claude Enterprise organizations, providing endpoints to read and configure Claude Tag settings on Slack channels and workspaces with Access bundle support. The API requires Admin API keys with read:claude_tag or write:claude_tag scope. This feature is exclusive to Claude Enterprise.
Manus plans best when given the destination, not the route. 'Find me 30 EU-based SaaS companies in the HR space with public funding rounds in 2025' beats 'first search Crunchbase, then…'.
I want a deliverable, not a chat. Here's the goal.
Goal:
"""
{{describe the deliverable: what it is, who it's for, what 'done' looks like}}
"""
Constraints:
- Time budget: {{e.g. "no more than 30 min of agent time"}}
- Quality bar: {{e.g. "every claim has a primary-source link"}}
- Format: {{Markdown / Google Doc / CSV with these columns / etc.}}
- Out of scope: {{things you should NOT spend cycles on}}
Run the plan. Hand me the deliverable when it's done.Manus is autonomous, not magical. The first few steps tell you whether the plan is on the right track. Cancel + re-prompt if it's drifting.
Don't act on autonomous output blind. Run a structured QA pass against the deliverable.
Audit the deliverable below for trustworthiness before I act on it.
Deliverable:
"""
{{paste Manus output}}
"""
Goal it was supposed to satisfy:
"""
{{paste original goal}}
"""
Output:
1. **Coverage** — did it actually answer the goal? Anything missing?
2. **Trust spot-checks** — pick 3 specific claims and tell me which I should verify by hand.
3. **Format issues** — does it match the requested format? Are headings, columns, links right?
4. **Red flags** — fabricated facts, broken links, hallucinated names. List specifically.
5. **Verdict** — ship as-is / fix list / re-run with a tighter prompt.
Be a skeptical reviewer, not a polite one.Replaced 3 hours of manual Crunchbase / LinkedIn search per week with a Manus run. Quality was within 90% on the first pass after the QA step caught 4 fabricated funding rounds. The QA gate is non-optional.
Manus hallucinates with the same physics as any LLM — just over more steps, with more confidence. The Claude QA step is the only thing keeping bad data out of your decisions.
'Research the EU SaaS market' is unbounded. Tight constraints make Manus useful; loose ones make it expensive.
What changed in AI tools and the stacks that ship. One email a week.