Blum45

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Blum45

10h ago

claude - the fastet moving contender for leadership
not everyone understands how much anthropic is crushing ai race
these crazy guys made 72 releases in just 52 days
moreover, many of these are absolute bangers
the main ones:
> models & core platform
- opus 4.6: upgraded flagship with 1m context
- sonnet 4.6: capable mid-tier model upgrade
- fast opus 4.6: experimental 2.5x faster flagship
- 1m context: massive context for paid plans
> developer tools
- agent teams: parallel agents for complex tasks
- auto mode: autonomous permission decisions with safeguards
- code review: multi-agent paralle

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GateUser-16cd01b4

To The Moon 🌕

Blum45

03-20 20:43

first time handing full control to "tech set to replace us"
what's the output product:

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Blum45

03-19 20:42

Good Stake -> Productive Agent
many believe that an AI agent is just a well-written prompt
beyond that, it is very important to select the proper agent pieces:
> LLM
> Tools
> Memory
> Triggers
> Feedback loop
not a single point - the agent is just an empty talker
1. LLM: the reasoning engine
this part defines objectives, course of action, and design of execution.
but LLM itself doesn't auto-access your systems, retain stable context, or act in the real world
that is why “just using GPT” is not the same as building an agent
2. Tools: the execution layer
it's hands for agent, this layer convert

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Blum45

03-14 19:27

3 common mistakes in AI agents + solutions
brush them off first -> they'll brush you off
so, fix upfront:
1. infinite tool call - resources drain even without your knowledge
agent calls tool, it fail, restart repeatedly until collapsing (Ralph Loop)
> fix: set limits + timeouts
2. forgets mid-flow - agent has amnesia
agent took a step, forgot previous, lost the point
> fix: Redis/JSON memory store with clear schema
3. silenced errors - disaster pileup
agent gets error, ignores, keeps accumulating errors, crashes.
user has no clue where to find the problem
> fix: audit log every step + roll

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Blum45

03-10 21:22

how to сreate OpenClaw skill in 3 min
skills are a key part of a quality AI agent
u can't do without them and custom skills are often essential
safety first: always test locally before production use
1. creating a directory
example (entered in terminal):
| mkdir -p ~/.openclaw/workspace/skills/skill_name
this is where all necessary data for the skill will be stored
2. SKILL·md (instruction for LLM)
the key factor is correctly formulating the essence of desired LLM action
this file uses YAML frontmatter for metadata and Markdown for instructions
important:
> make it executable, specific, and co