Blum45

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AI tools that actually help you grow X:
> TweetHunter
an AI platform that can:
- writes personalized drafts
- rewrites existing ones
- suggests ideas based on your niche
- gives you a scheduler with automation
- runs performance analytics + highlights the best posting times
> NoimosAI
fully autonomous AI marketing platform (runs on agents that work 24/7):
- analyzes your brand data
- creates social content
- replies to comments
- adapts to your style
- tracks engagement
> bonus tool: PostEverywhere
AI platform for social media management, features:
- cross-posts the same content with auto-opt

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Blum45

04-03 19:21

TOP LLMs BY USE CASE
rn there's no super universal model for all tasks
models are now sharpening for specific use cases
so using different models for different needs yields better results and boosts work efficiency
built from Arena (human-voted) + LiveBench (clean benchmark) data
> general chat and reasoning
top models for dialogues, Q&A, text analysis
- Claude Opus 4.6 Thinking
- Gemini 3.1 Pro Preview
> coding
cutting-edge for code gen, refactoring, debug, algo tasks
- Claude Opus 4.6 Thinking
- GPT 5.4 High
> vision
leaders in diagram/table analysis, visual description
- Claude Opus 4.6
- G

GLM0,54%

NANO2,94%

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Blum45

04-01 20:55

10 BEST LOCAL LLMs MODELS
lbh, everyone's tired of paying huge money for AI subs
local models are our saviors in this matter
total data privacy + no sub fees ever
so, here are the top picks:
> GLM-5
top open-weight model rn
elite code + long context, but requires serious GPUs
> MiniMax M2.5
built for real-world use
quick, robust tool-use, always thinks answers through
> Qwen3.5-27B
best single-GPU model
fast, honest, great instruction following
> Qwen3.5-397B-A17B
alibaba flagship
strong reasoning + math, similar to closed models in this regard
> DeepSeek V3.2
coding beast
state-of-the-art be

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Blum45

03-31 18:50

HOW TO CRAFT TRULY EFFECTIVE AI PROMPTS
you ask an LLM to for a high-quality report
and get back text written with expert-level confidence
but packed with total BS
familiar?
so, to avoid situations like this, you need to understand
these basic points:
> the “smart but unreliable” assistant problem
LLM output is 20% the model, 80% how you structure the prompt
prompt engineering - just hardcore natural language computing control
so, to get quality output, you need to stop chatting with the model and start programming it
> AI hallucinations - indicator of insufficient instructions
to ensure groun

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Blum45

03-25 19:40

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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Netcafe:

Let's go

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