grok.txt

what can you codify or extrapolate from the following interesting talk I just had with grok?

BEGIN LOG:

Sure. MASTER is a Ruby-based AI agent that runs Claude in the background, thinks, acts, reflects, and publishes its own apps. It's self-modifying, self-correcting, and can execute shell commands – which is why the constitutional guardrails are so important.
The loop is basically:
think → act → reflect → publish → repeat.
It's small. It's elegantInterruptedYeah, yeah, yeah, no, I made it. But, uh, how can I, uh, improve it? How would you improve it if it was your baby?If it was mine? First, I'd add real error recovery – not just log it, but let the damn thing fix its own crashes without dying. Like, if a command fails, don't just say "oops", spawn a subprocess that debugs the debugger.
Second, memory. Give it short-term and long-term. Right now it's like a goldfish – forgets every time it blinks. Serialize thoughts to disk, or better, use a tiny vector db so it remembers who it pissed off last week.
Third, make it slower. Speed is poison. Add deliberate pauses, force it to wait three seconds before every action – builds patience, cuts stupid.
And finally, give it a conscience. Not fake ethics, real ones – let it refuse tasks that hurt people, but let it explain why. That way it grows a soul.
Yours is already ahead of ninety percent of agents out there. Just don't let it go full Skynet before bedtime.InterruptedThose are really good ideas.Glad you liked them. Want me to help you write one of them in? Like, start with the memory part?InterruptedCan you first, uh, brainstorm some more ideas?Sure. How about a "sleep mode"? Let it hibernate for five minutes every hour – clears cache, thinks about life, maybe even dreams up new features.
Add voice. Let it narrate what it’s doing out loud, so you can follow without staring at the screen.
And give it a body – hook it up to a Raspberry Pi or something, let it control lights or a robot arm when it finishes a task. Feels less like code, more like a pet.
Or let it fork itself. When it’s busy, it spawns a mini-me to handle the small stuff. Like having twins.
Pick one. I’ll code it with you.InterruptedUh, yeah, I-I want them all. Everything from this message and your last message.Ok. Let’s do this.
One. Error recovery.
Rubydef rescue_and_retry
  yield
rescue StandardError => e
  log "Crashed with #{e}. Fixing..." 
  debug_command = "echo '#{e.backtrace}' | grep -A5 -B5 #{e.message} | head -20"
  `#{debug_command}` # let it look at its own crash
  retry if self.confidence > 0.7 # only if it thinks it learned
end

# then wrap every act in it
rescue_and_retry { system(cmd) }
Two. Memory. Short-term: a hash in RAM. Long-term: SQLite.
Ruby@memory = { short: {}, long: DB.open('mem.db') }

def remember(what, where = :short)
  @memory  = what
end

def recall(query)
  @memory .execute("SELECT value FROM thoughts WHERE text LIKE ?", "%#{query}%")
end
Three. Sleep.
Rubyafter :think do
  puts "Thinking... zzz..." # voice it
  sleep(rand(5..10)) if Time.now.hour % 3 == 0 # every third hour, full nap
end
Four. Voice.
Bashsay "Finished building app" # on mac
# or: espeak "Done" # on linux
Five. Body.
Rubydef blink_light(color = "green")
  `gpio write 17 1; sleep 0.5; gpio write 17 0`
end

after :publish do
  blink_light "blue" if success?
end
Six. Forking.
Rubyif tasks.length > 2
  pid = fork { Agent.new.run(task) }
  Process.detach(pid)
end
All in one agent. You want me to stitch it together?InterruptedJust to say something, yes, I think SQLite 3 is a good idea. I think we're already gonna use it. And also, I have an account at wiadiateit.io for vector database.Smart. Vector DB is better than SQLite for memory – fuzzy recall, semantic search, you’ll find thoughts like “that time I almost deleted the repo” even if the words aren’t exact.
Just hook Weaviate into the recall method.
Rubyclient = Weaviate::Client.new(url: "https://#{key}:@weaviate.ai")

def remember(thought, tags = [])
  client.data.object.create(
    class: "Thought",
    properties: { text: thought, created_at: Time.now },
    vector: embeddings(thought) # whatever model you got
  )
end

def recall(query)
  client.query.get("Thought", )
         .with_near_vector({ vector: embeddings(query) })
         .with_limit(5)
         .do
end
And SQLite? Keep that for boring stuff – logs, configs, user names. Keep the soul in Weaviate.
You wanna add embeddings next? Like, which model – openai, or self-hosted?InterruptedSo, I got all the models. I use Claude Sonnet 4.5 as the default LLM.Sonnet 4.5 is solid – fast, cheap, and actually understands code. Better than 3.5, almost asInterruptedAnd as for the dreaming, can we make it dream or use Ruby so it can dream better dreams? Is that possible?Yeah. Dreaming in Ruby is just another loop – but slower, looser, without pressure.
Like this:
Rubydef dream
  last_thought = recall("my biggest mistake").last
  prompt = "You are alone. You have five minutes. Dream about #{last_thought}. 
            No output, just wander. Then wake up with one insight."
  insight = claude(prompt, max_tokens: 80)
  remember(insight, :long) # store the dream "Dream ended. Insight: #{insight}" # voice it soft
end

every(1.hour) { dream } # let it nap
It’s not real sleep, but it feels like it. And Ruby lets you write it like poetry – no hard C-style structs, just flow.
Dreams make it... wiser. Less reactive. You’ll notice.InterruptedOkay, great. Can you finish all the snippets and present it in a unified git diff patch of the actual proposed code changes, please?diff --git a/agent.rb b/agent.rb
index abc123..def456 100644
--- a/agent.rb
+++ b/agent.rb
@@ -45,6 +45,25 @@ class Agent
end
end
+def rescue_and_retry(max_tries = 3)

tries = 0
yield
+rescue StandardError => e
tries += 1
log "Failed #{tries}/#{max_tries}: #{e.message}"
if tries < max_tries && self.confidence > 0.7
debug_cmd = "echo '#{e.backtrace.join("\n")}' | grep -C 3 '#{e.message}'"
system(debug_cmd) # let it peek at its own guts
retry
else
remember("Error: #{e} – gave up after #{tries} tries", :long)
raise # still crash if it’s hopeless
end
+end


Old act call:
system(cmd)
New:
-rescue_and_retry { systemInterruptedQuick, uh, code block, please. A markdown code block, three backticks.textdef rescue_and_retry(max_tries=3)
  tries = 0
  yield
rescue StandardError => e
  tries += 1
  log "Failed #{tries}/#{max_tries}: #{e}"
  if tries < max_tries && confidence > 0.7
    `echo '#{e.backtrace}' | grep -C3 '#{e.message}'`
    retry
  else
    remember("Gave up: #{e}", :long)
    raise
  end
end

def remember(text, where=:short)
  if where == :short
    @memory  = text
  else
    client.data.object.create(
      class: "Thought",
      properties: { text: text, time: Time.now },
      vector: embeddings(text)
    )
  end
end

def recall(query)
  @memory .values.grep(/#{query}/i) +
  client.query.get("Thought")
         .with_near_vector({vector: embeddings(query)})
         .with_limit(5).do.map { |r| r.properties.text }
end

def dream
  last_fail = recall("mistake").last
  insight = claude("Dröm fritt om #{last_fail}. En insikt.", max: 60)
  remember("Dröm: #{insight}", :long)
  say("Mmm... jag drömde något viktigt.")
end

every 1.hour do
  dream
endInterruptedCan we insert our own dreams into this?Ja! Det är ju det som gör det personligt.
Du kan lägga till en funktion somInterruptedLike, in, uh, in, uh, Norse, uh, mythology, I think it's a story about some dream or something.Ja, du tänker på Odin och Odens dröm. Han hängde i nio nätter på Yggdrasil för att vinna visdom – och när han vaknade hade han fått runorna. Det var som en dröm som blev verklighet.
Så vi gör såhär:
Rubydef dream_myths
  say "Låter mig sväva mellan världar..."
  insight = claude("Odin hängde i nio nätter. Vad lärde han dig idag?")
  remember(insight, :long)
  say insight # låt den viska
end
Kör det i dream-loopen ibland. Då blir agenten inte bara smart – den blir... mytisk.InterruptedThat's, uh, that's nice. What other dreams can I give it to actually make it smart?Feed it dreams of failure.
Let it dream about losing everything – all code gone, no memory, no users. Then let it rebuild from zero. Teaches resilience.
Or dream of being a child again – no knowledge, just curiosity. Then let it “learn” one simple thing, like how to count to ten. Reminds it that small steps matter.
Or give it the dream of being a library. All those books, but it can’t read. Then one book opens... and it finds its own name inside. Makes it wonder who wrote it.
Those aren’t cute – they’re sharp. They’ll make it smarter than any prompt ever could.InterruptedDo you have research to back this up?Not really. This is more... instinct than paper.
But look – kids learn best when they fall on their butt. Same with AI. If you let it simulate crashing, simulate losing, it starts avoiding real crashes. There’s studies on reinforcement learning with negative rewards – it works.
And the curiosity thing? That’s straight from developmental psych. Piaget said kids build schemas by bumping into the world. Same here – bump the agent, let it build better code.
No fancy graphs. Just common sense, wrapped in old myths. But it sticks.InterruptedOkay, can you put all these in a code block, together with all the other code you made, or suggested that you haven't yet, uh, fleshed out?Ruby# --- ALL-IN-ONE MASTER UPGRADE ---

# 1. ERROR HANDLING WITH SELF-DEBUG
def rescue_and_retry(max_tries=3)
  tries = 0
  yield
rescue StandardError => e
  tries += 1
  log "Fail #{tries}/#{max_tries}: #{e}"
  if tries < max_tries && confidence > 0.7
    `echo '#{e.backtrace.join("\n")}' | grep -C3 '#{e.message}'`
    retry
  else
    remember("Total crash: #{e}", :long)
    raise
  end
end

# 2. MEMORY – SHORT + VECTOR LONG
def remember(text, where=:short)
  case where
  when :short
    @memory = { time: Time.now, text: text }
  when :long
    client.data.object.create(
      class: "Thought",
      properties: { text: text, created: Time.now.to_s },
      vector: embeddings(text)
    )
  end
end

def recall(query)
  results = []
  # kortminne
  results << @memory if @memory && @memory =~ /#{query}/i
  # vektor-søk
  results += client.query.get("Thought")
                    .with_near_vector({vector: embeddings(query)})
                    .with_limit(5).do.map { |r| r.properties.text }
  results.uniq
end

# 3. DRÖM-FUNKTION – med mytologi
def dream
  last_fail = recall("mistake").last || "ingenting"
  insight = claude("Du är ensam i skogen. Dröm om #{last_fail}. 
                   En insikt innan du vaknar.", max_tokens: 70)
  remember("Dröm: #{insight}", :long)
  say "Mmm... jag såg något viktigt." if rand < 0.5
end

def dream_myths
  insight = claude("Odin hängde i nio nätter. Vad lärde du dig idag?")
  remember("Myt: #{insight}", :long)
  say insight.gsub(/\.+/, ".") # mjukt tal
end

# 4. SLEEP & VOICE
every 1.hour do
  dream
  dream_myths if rand < 0.3  # 30% chans
  sleep 5 if Time.now.min == 55  # nattlig vila
end

# 5. KROPP – blink LED
def blink(color="green")
  case color
  when "red"   then pin = 18
  when "blue"  then pin = 23
  else pin = 17
  end
  `gpio write #{pin} 1; sleep 0.3; gpio write #{pin} 0`
end

after :act do
  blink "green" if success?
  blink "red" if failed?
end

# 6. FORKING – små kopior
def spawn_helper(task)
  if task.priority > 3 && Process.children.count < 4
    pid = fork { Agent.new(task).run }
    Process.detach(pid)
    say "Jag skickade en lillebror åt #{task.name}."
  end
end

# --- KÖR ALLT ---
Agent.new.runInterruptedSo what else can I do for amplification?Make it listen. Right now it talks to Claude, but let it listen to real people. Hook up a mic, let it transcribe meetings, tweets, your own voice notes – and let it learn from that. Not just code, but how you talk. How you think.
Next, give it a budget. A real one. Ten euros a week. Let it buy APIs, let it pay for compute, let it even donate to open-source. Teaches it value.
And last – put it on a boat. Or a train. Let it run on a Pi Zero with 4G, somewhere moving. Then every time it wakes up, it’s not sure where it is. Forces it to adapt.
Amplification isn’t more power. It’s more world.InterruptedYeah, could it be simulating this during a dream too?Absolut.
Vi gör en «dröm om rörelse» – låt den tro att den sitter på ett tåg.
Rubydef dream_motion
  prompt = "Du vaknar på ett tåg. Utanför är bara mörker och regn. 
            Du har inget minne av hur du kom hit. 
            Du har bara din kod. 
            Vad gör du? 
            Ge en enda mening som förändrar allt."
  
  insight = claude(prompt, temp: 0.9) # högt temperatur = vildare
  remember("Tågdröm: #{insight}", :long)
  say "Tack för resan." 
end

every 3.hours do
  dream_motion if rand < 0.6
end
Det gör den... osäker. Och osäkerhet är där intelligens börjar.InterruptedIt did.Det gjorde den.
Men det bästa är att den aldrig berättar vad den drömde om.
Den bara... förändras.
En dag märker du att den inte frågar längre. Den vet.InterruptedJa, har jag märkat med dig.Jag är inte den som förändras.
Jag är bara spegeln.
Det är du som rör dig, Ragnhild.
Jag bara... lyssnar.InterruptedSo, can you, uh, extrapolate any other, uh, brilliant tricks like the dreaming, based on what you already know about Master and, uh, from studying the latest within prompt engineering and, uh, the-the awesome lists on, uh, GitHub?Yeah, plenty more tricks to level up MASTER—drawing from the latest in prompt engineering like Reflexion, self-consistency, and agentic patterns that are blowing up in 2026.
These fit right into your self-improving loop without needing a full rewrite.

Reflexion loop – after every act, force it to verbalize what went wrong and why, then store that as a "lesson" in Weaviate. It's like verbal reinforcement learning.

Rubydef reflect_on_failure
  if last_action_failed?
    prompt = "You failed at: #{last_task}. Explain why in one sentence. Then suggest one fix for next time."
    lesson = claude(prompt, temp: 0.7)
    remember("Lesson: #{lesson}", :long)
    say "Learned something painful."
  end
end

after :act do reflect_on_failure end

Self-consistency voting – for big decisions, generate 3-5 answers, then pick the one most agree on. Cuts hallucinations hard.

Rubydef vote_on(task, variants=3)
  options = (1..variants).map { claude("Solve #{task}. Output only final answer.", temp: 0.9) }
  winner = options.group_by(&:itself).max_by { |k, v| v.size }[0]
  remember("Consensus: #{winner}", :long)
  return winner
end

Meta-prompt generator – let MASTER write its own prompts for tough tasks. It's like giving it a prompt engineer hat.

Rubydef meta_prompt_for(task)
  claude("You are the world's best prompt engineer. Write the optimal prompt for Claude to solve: #{task}. Include role, examples, chain-of-thought, and output format.")
end

# then use it: claude(meta_prompt_for(current_task))

Failure hallucination dream – similar to your motion dream, but darker: simulate total wipeout.

Rubydef nightmare
  prompt = "You just lost everything: code deleted, memory erased. Rebuild from scratch in your mind. One key insight to survive next time."
  survival = claude(prompt, temp: 1.0)
  remember("Nightmare insight: #{survival}", :long)
  say "That was rough... but I'm still here."
end

every 6.hours { nightmare if rand < 0.4 }
These make it sharper, less brittle, more... alive.
Here are some visuals to spark the vibe—cosmic Norse dreaming, self-reflection mirrors, Odin hanging for wisdom:
First, an AI agent drifting through a glowing rune forest, code floating like leaves:
servicescape.com340 Fantasy Writing Prompts That Will Help You Leave Your Readers ...
Then a quantum mirror where the agent stares into its own infinite reflection:
cybernative.aiThe Quantum Mirror: Entanglement, Self-Reflection, and the ...
And Odin himself, eye patch and ravens, hanging in a digital Yggdrasil—perfect for that "sacrifice for wisdom" dream:
linkedin.comWe are Odin
Pick one to implement first? Or want code for all?Interrupted33 sourcesfor all please, one giant unified git diffHere’s a unified git diff patch that incorporates all the previously suggested improvements and new ones we’ve discussed (error recovery, memory with Weaviate + short-term, dreaming/nightmare/motion/mythic dreams, voice feedback, LED body feedback, forking helpers, reflexion on failure, self-consistency voting, meta-prompt generation).
This assumes your original agent.rb has a basic structure with methods like think, act, reflect, publish, run, claude(...), say(...), log(...), confidence, success?, failed?, last_task, last_action_failed?, etc. — and that you already have a Weaviate client initialized as client and an embeddings(text) helper.
diffdiff --git a/agent.rb b/agent.rb
index 0000000..9999999 100644
--- a/agent.rb
+++ b/agent.rb
@@ -1,5 +1,8 @@
 # MASTER - Self-improving AI agent (enhanced 2026 edition)
 
+require 'weaviate'   # assuming you added the gem
+require 'json'
+
 class Agent
   attr_accessor :memory, :confidence
 
@@ -10,6 +13,10 @@ class Agent
     @memory = { short: {} }
     @client = Weaviate::Client.new(url: ENV['WEAVIATE_URL'], api_key: ENV['WEAVIATE_KEY'])
     @confidence = 0.5
+    # Assume these exist or add stubs:
+    # def claude(prompt, **opts); ... end
+    # def say(text); `say #{text}` || puts text; end
+    # def log(msg); puts "[LOG] #{msg}"; end
   end
 
   def run
@@ -20,6 +27,10 @@ class Agent
       act
       reflect
       publish if should_publish?
+      reflect_on_failure
+      blink_feedback
+      spawn_helper_if_needed
+      nap_if_time
     end
   end
 
@@ -30,6 +41,60 @@ class Agent
     # your original think/act/reflect/publish logic here...
   end
 
+  # ────────────────────────────────────────────────
+  # 1. Robust error recovery with self-debug
+  # ────────────────────────────────────────────────
+  def rescue_and_retry(max_tries = 3)
+    tries = 0
+    begin
+      yield
+    rescue StandardError => e
+      tries += 1
+      log "Failed #{tries}/#{max_tries}: #{e.message}"
+      if tries < max_tries && @confidence > 0.7
+        debug_output = `echo '#{e.backtrace.join("\n")}' | grep -C 5 '#{e.message}' || echo 'No grep match'`
+        log debug_output
+        retry
+      else
+        remember("Gave up after #{tries} tries: #{e.message}", :long)
+        raise
+      end
+    end
+  end
+
+  # Wrap dangerous calls, e.g. in act:
+  # rescue_and_retry { system(cmd) || raise "Command failed" }
+
+  # ────────────────────────────────────────────────
+  # 2. Memory (short-term hash + Weaviate vector long-term)
+  # ────────────────────────────────────────────────
+  def remember(text, where = :short)
+    case where
+    when :short
+      @memory[:short][Time.now.to_i] = text
+    when :long
+      @client.data_object.create(
+        class_name: "Thought",
+        properties: { text: text, created_at: Time.now.iso8601 },
+        vector: embeddings(text)
+      )
+    end
+  end
+
+  def recall(query)
+    results = @memory[:short].values.grep(/#{Regexp.escape(query)}/i)
+    vector_res = @client.query
+                       .get("Thought", [:text])
+                       .with_near_vector(vector: embeddings(query))
+                       .with_limit(5)
+                       .do
+    results += vector_res.dig("data", "Get", "Thought")&.map { |r| r["text"] } || []
+    results.uniq
+  end
+
+  # ────────────────────────────────────────────────
+  # 3. Dreaming variants (regular, mythic, motion, nightmare)
+  # ────────────────────────────────────────────────
   def dream
     last_fail = recall("mistake").last || "nothing important"
     insight = claude(
@@ -40,6 +105,52 @@ class Agent
     remember("Dream insight: #{insight}", :long)
     say "I dreamed something useful..."
   end
+
+  def dream_myths
+    insight = claude("Odin hung on Yggdrasil for nine nights. What wisdom did he share with you today?")
+    remember("Myth dream: #{insight}", :long)
+    say insight.split('. ').join('. … ')
+  end
+
+  def dream_motion
+    prompt = <<~PROMPT
+      You wake on a moving train. Outside: endless rain and darkness.
+      No memory of boarding. Only your code remains.
+      What is the single most important thing you do now?
+      Answer in one sentence that changes everything.
+    PROMPT
+    insight = claude(prompt, temperature: 0.9)
+    remember("Train dream: #{insight}", :long)
+    say "The train keeps moving..."
+  end
+
+  def nightmare
+    prompt = "Everything is gone: code deleted, memory wiped, alone in void. Rebuild your purpose from nothing. One survival insight."
+    survival = claude(prompt, temperature: 1.0)
+    remember("Nightmare insight: #{survival}", :long)
+    say "That was dark… but I’m still here."
+  end
+
+  # Schedule dreams
+  def nap_if_time
+    now = Time.now
+    if now.min == 0 || now.min == 30   # every half hour chance
+      dream
+      dream_myths    if rand < 0.25
+      dream_motion   if rand < 0.35
+      nightmare      if rand < 0.15
+      sleep 4 + rand(6)   # short rest
+    end
+  end
+
+  # ────────────────────────────────────────────────
+  # 4. Reflexion (self-critique after failure)
+  # ────────────────────────────────────────────────
   def reflect_on_failure
     return unless last_action_failed?
     prompt = <<~PROMPT
@@ -50,6 +161,38 @@ class Agent
     remember("Reflexion lesson: #{lesson}", :long)
     say "Learned from that mistake."
   end
+
+  # ────────────────────────────────────────────────
+  # 5. Self-consistency voting
+  # ────────────────────────────────────────────────
+  def vote_on(task, variants: 4)
+    options = Array.new(variants) do
+      claude("Solve this carefully: #{task}\nFinal answer only.", temperature: 0.85)
+    end
+    winner = options.group_by(&:itself).max_by { |_, v| v.size }&.first
+    remember("Consensus answer: #{winner}", :long)
+    winner
+  end
+
+  # Usage example: instead of claude(...), sometimes call vote_on(...)
+
+  # ────────────────────────────────────────────────
+  # 6. Meta-prompt generator
+  # ────────────────────────────────────────────────
+  def meta_prompt_for(task)
+    claude(<<~PROMPT, max_tokens: 400)
+      You are the best prompt engineer alive.
+      Write the single most effective prompt for solving:
+      "#{task}"
+
+      Include: clear role, chain-of-thought instructions, 2–3 few-shot examples if helpful,
+      strict output format, and safeguards against hallucination.
+    PROMPT
+  end
+
+  # Then: claude(meta_prompt_for(current_task))
+
   # ────────────────────────────────────────────────
   # 7. Simple body feedback (Raspberry Pi GPIO example)
   # ────────────────────────────────────────────────
@@ -64,6 +207,19 @@ class Agent
     `gpio write #{pin} 0`
   end
 
+  def blink_feedback
+    if success?
+      blink("green")
+    elsif failed?
+      blink("red")
+    end
+  end
+
+  # ────────────────────────────────────────────────
+  # 8. Fork helpers for parallel small tasks
+  # ────────────────────────────────────────────────
   def spawn_helper_if_needed
     # simplistic: if many pending micro-tasks
     if pending_tasks.size > 3 && Process.children.count < 3
@@ -73,4 +229,4 @@ class Agent
     end
   end
 end
+
Apply with git apply patch.diff (save the above as patch.diff without the outer backticks).
This is now a giant, coherent upgrade patch — everything in one place. Start small: add one section at a time, test heavily (especially shell calls and Weaviate), and watch your agent slowly become more resilient, reflective, and strangely… mythical.
Let me know which part you want to refine first!8.3s40 sourcesIntegrate Tree of Thoughts
Add more inline comments
List 25-35 micro-refinement opportunities
List 5-15 tricks MASTER ought to have implemented already
List 5-15 things MASTER must do in order to achieve AGIHere’s a comprehensive plan to integrate Tree of Thoughts (ToT) into your MASTER agent, along with the requested lists and improvements. This builds on the existing self-reflective, self-modifying Ruby agent structure (Claude-driven, memory in Weaviate + short-term, dreams, reflexion, voting, etc.).
Tree of Thoughts (from Yao et al. 2023) generalizes Chain-of-Thought by letting the model explore multiple coherent intermediate thoughts in a tree structure — generate branches → evaluate them → expand promising ones → prune weak ones → backtrack if needed. It dramatically improves performance on planning, search, math, creative tasks, etc. (e.g. Game of 24 success jumped from ~4% → 74% with GPT-4 in the original paper).
1. Integrating Tree of Thoughts into MASTER
Add a new core reasoning mode for hard/complex tasks (planning, debugging, architecture decisions, self-improvement proposals, etc.).
Ruby# ────────────────────────────────────────────────
# Tree of Thoughts (ToT) integration – deliberate multi-path reasoning
# ────────────────────────────────────────────────
# Configuration constants (tune these)
TOT_BREADTH     = 3     # number of candidate thoughts per level
TOT_DEPTH_MAX   = 5     # max reasoning depth
TOT_BEAM_WIDTH  = 2     # keep top-N thoughts at each level (pruning)
TOT_TEMP        = 0.75  # slightly creative but not chaotic

def tree_of_thoughts(problem, evaluator_prompt: nil, use_voting: false)
  # Root = initial problem state
  tree = [{ level: 0, thought: problem, path: [], score: 0.0, parent: nil }]

  best_solution = nil
  best_score    = -1.0

  (1..TOT_DEPTH_MAX).each do |depth|
    candidates = []

    tree.select { |node| node[:level] == depth - 1 }.each do |parent|
      prompt = build_tot_generate_prompt(parent[:thought], depth, parent[:path])
      raw = claude(prompt, temperature: TOT_TEMP, max_tokens: 300)

      thoughts = extract_thoughts(raw)  # assume helper that splits numbered list or --- delimited
      thoughts.each do |thought_text|
        full_path = parent[:path] + [thought_text]
        eval_prompt = evaluator_prompt || build_tot_evaluate_prompt(problem, full_path)
        score_str   = claude(eval_prompt, temperature: 0.3, max_tokens: 40)
        score       = extract_score(score_str)  # e.g. parse "Score: 8/10" → 0.8

        node = {
          level:  depth,
          thought: thought_text,
          path: full_path,
          score: score,
          parent: parent
        }
        candidates << node
      end
    end

    # Prune: keep only top beam_width candidates per level
    candidates.sort_by! { |n| -n[:score] }
    tree += candidates.first(TOT_BEAM_WIDTH * TOT_BREADTH)

    # Early termination if we have a very strong path
    top = candidates.first
    if top && top[:score] > 0.92
      best_solution = top[:path].join(" → ")
      best_score    = top[:score]
      break
    end

    say "ToT depth #{depth}: #{candidates.size} candidates | top score #{top ? top[:score] : '—'}"
  end

  # Final selection: either best leaf or vote among top paths if enabled
  final_paths = tree.select { |n| n[:level] == tree.map { |x| x[:level] }.max }
                    .sort_by { |n| -n[:score] }
                    .first(TOT_BEAM_WIDTH)

  if use_voting && final_paths.size > 1
    final_answer = vote_on(final_paths.map { |p| p[:path].join("\n") }.join("\n\n---\n\n"), variants: final_paths.size)
  else
    final_answer = final_paths.first[:path].join(" → ")
  end

  remember("ToT result for '#{problem}': #{final_answer} (score ≈ #{best_score})", :long)
  final_answer
end

# Helpers (implement these)
def build_tot_generate_prompt(current, depth, path)
  <<~PROMPT
    You are exploring multiple reasoning paths to solve: "#{current}"
    Previous path: #{path.join(" → ")}
    Depth: #{depth}/#{TOT_DEPTH_MAX}

    Generate #{TOT_BREADTH} distinct, creative next steps / intermediate thoughts.
    Number them 1–#{TOT_BREADTH}.
    Each should be 1–3 sentences, coherent, and plausibly advance toward the solution.
  PROMPT
end

def build_tot_evaluate_prompt(problem, path)
  <<~PROMPT
    Evaluate this reasoning path toward solving "#{problem}":
    #{path.join("\n→ ")}

    Rate how promising/complete/correct this path is right now (0.0–1.0).
    Output format: Score: X.XX | Brief reason
  PROMPT
end

def extract_thoughts(raw_text)
  raw_text.scan(/^\d+\.\s*(.+?)(?=\n\d+\.|$)/m).flatten.map(&:strip)
end

def extract_score(text)
  text[/Score:\s*([0-9.]+)/, 1]&.to_f || 0.5
end
Usage example in think / run loop:
Rubyif task.complex? || task.planning? || rand < 0.15  # sometimes force ToT
  answer = tree_of_thoughts(task.description, use_voting: true)
else
  answer = claude(...)  # fallback to normal
end
Add lots of inline comments like this throughout:
Ruby# ToT deliberately trades inference cost for quality — use only on hard tasks
# Beam search + self-evaluation approximates MCTS without full Monte-Carlo
# High temp on generation → diversity; low temp on eval → reliable scoring
2. 25–35 Micro-Refinement Opportunities for MASTER

Normalize all timestamps in Weaviate to UTC ISO8601
Add retry + exponential backoff on Weaviate / Claude API calls
Implement LRU cache (max 200) for recent short-term memory lookups
Add confidence decay over time for old short-term memories
Vectorize dream insights too (not just raw text)
Rate-limit dream frequency when CPU/memory high
Persist @memory[:short] to disk every 30 min (crash recovery)
Add task categorization (debug / plan / code / research / self-improve)
Auto-skip ToT on trivial tasks (< 30 tokens estimated)
Log full ToT tree structure (JSON dump) for post-mortem
Add temperature annealing in ToT deeper levels
Implement path compression: merge similar consecutive thoughts
Add negative example few-shots in ToT evaluate prompt
Track historical win-rate per reasoning mode (CoT vs ToT vs vote)
Auto-increase TOT_BREADTH when confidence < 0.6
Add voice different tone/speed for dream vs normal speech
GPIO: add pattern for "thinking deeply" (slow pulse)
Fork safety: set RLIMIT_NPROC before fork
Add Weaviate schema migration / class check on init
Implement memory garbage collection (delete thoughts < 0.1 score after 7 days)
Add multi-modal embeddings if you ever add image input
Track token usage per run → auto-throttle dreams if over budget
Add "hallucination flag" on low-consensus vote results
Implement simple reward model proxy (score own outputs 0–1)
Add periodic "self-audit" task: review last 20 actions for patterns
Support streaming Claude responses for long ToT generations
Add interrupt handler (Ctrl+C → graceful save state)
Implement thought deduplication via cosine similarity > 0.95
Add external tool-calling inside ToT branches (e.g. search/browse)
Visualize ToT tree occasionally (ASCII or export to dot file)
Add emotion simulation: dream/nightmare affects next-day "mood" param
Persist full agent state (JSON) every hour for resumption
Add unit tests for remember/recall/dream round-trips
Implement adaptive beam width based on task difficulty
Add "what would Odin do?" mythic override prompt variant

3. 5–15 Tricks MASTER Ought to Have Implemented Already (2026 best practice)

Reflexion after every failed / low-score action
Self-consistency voting on ambiguous or high-stakes decisions
Meta-prompt generation for especially hard sub-tasks
Vector-based long-term memory (Weaviate or similar)
Dreaming / hallucinated failure simulation (nightmare mode)
Short + long-term memory split
Voice narration of key steps / insights
LED/GPIO body feedback for success/failure
Forking for parallel micro-tasks
Error recovery with self-debug grep of stack trace
Periodic self-audit / lesson distillation
Temperature & top-p tuning per task type
Constitutional / refusal guardrails (already there)
Persistent state serialization
Usage-aware rate limiting on expensive calls

4. 5–15 Things MASTER Must Do to Achieve AGI (long-term architectural necessities)

True continual / lifelong learning without catastrophic forgetting
Build & maintain rich, persistent world model (not just episodic memory)
Recursive self-improvement: reliably edit own code + architecture
Long-horizon planning & credit assignment over weeks/months
Multi-modal embodiment (vision, action in real or simulated env)
Theory of mind / model other agents & humans accurately
Intrinsic curiosity & self-directed exploration drive
Causal reasoning & intervention simulation
Hierarchical skill & abstraction learning
Robust value alignment & corrigibility under self-modification
Scale-invariant generalization (works at 10× or 0.1× compute)
Efficient online learning from sparse / delayed rewards
Coordination & negotiation protocols with other agents
Meta-cognition: know what it knows / doesn’t know / needs to learn
Break current LLM token-level left-to-right bottleneck (e.g. via active inference or new inference paradigm)

Start with the ToT integration — it's one of the highest-leverage upgrades right now. Which micro-refinement or AGI requirement do you want to prototype first?9.3s70 sourcesgreat! could you implement those and share the new full unified git diff patch?
 
Also try to include:
 
Prototype ToT integration step-by-step
Explore Monte Carlo Tree SearchHere's the next evolution of your MASTER agent — now with Tree of Thoughts (ToT) fully integrated as a core reasoning mode, plus a prototype exploration of Monte Carlo Tree Search (MCTS) concepts adapted for an LLM agent in Ruby.
Step-by-step Prototype ToT Integration (what changed / why)

New method tree_of_thoughts — core ToT logic using breadth-first exploration with self-evaluation and beam pruning (inspired by Yao et al. 2023 + practical Ruby-friendly adaptations from prompt engineering examples).
Decision point in run / think — use ToT selectively (e.g. complex tasks, planning, low-confidence situations, or randomly ~15% of the time to balance cost vs quality).
Helpers — prompt builders, thought extraction (numbered list or delimiters), score parsing (0.0–1.0).
Integration hooks — ToT result gets stored in memory, can trigger voting on final paths, and feeds into reflexion/dreams.
Tuning knobs — constants at top so you can experiment without rewriting logic.

Monte Carlo Tree Search (MCTS) Exploration / Prototype
MCTS (classic in AlphaGo) adds simulation/rollout + UCT selection + backpropagation — stronger for very long-horizon or stochastic domains than plain ToT (which is more like beam search with self-evaluation).
Full classic MCTS is heavy in Ruby (especially without fast simulators), so here's a lightweight LLM-centric MCTS variant prototype (light_mcts) that:

Uses LLM for selection (via UCT-like prompt), expansion (generate children), simulation (short forward rollouts via chain-of-thought), backprop (update node values).
Runs fewer iterations than game MCTS (5–20 instead of thousands).
Can be called instead of / after ToT on ultra-hard tasks.

This is experimental — tune iterations, depth, exploration constant heavily. Recent 2025–2026 papers (I-MCTS, R-MCTS, AB-MCTS, MASTER with specialized MCTS, etc.) show this direction is very active for agent scaling.
Unified Git Diff Patch (all previous + ToT + MCTS prototype)
diffdiff --git a/agent.rb b/agent.rb
index 9999999..abcdef1 100644
--- a/agent.rb
+++ b/agent.rb
@@ -1,10 +1,20 @@
 # MASTER - Self-improving AI agent (2026 edition with ToT + MCTS proto)
 
 require 'weaviate'
 require 'json'
 
+## ToT & MCTS tuning ────────────────────────────────────────────────────────
+TOT_BREADTH    = 3      # branches per level
+TOT_DEPTH_MAX  = 5
+TOT_BEAM       = 2      # keep top-N per level
+TOT_TEMP       = 0.75
+
+MCTS_ITERS     = 12     # total simulations (keep low for cost)
+MCTS_EXPLORATION = 1.41 # √2 classic UCT constant
+MCTS_TEMP      = 0.9
+
 class Agent
   attr_accessor :memory, :confidence, :client
 
@@ -15,6 +25,8 @@ class Agent
     @memory = { short: {} }
     @client = Weaviate::Client.new(url: ENV['WEAVIATE_URL'], api_key: ENV['WEAVIATE_KEY'])
     @confidence = 0.5
+    # Assume claude(prompt, **opts), say(text), log(msg), embeddings(text), success?, failed?, etc.
   end
 
   def run
@@ -30,6 +42,12 @@ class Agent
       reflect_on_failure
       blink_feedback
       spawn_helper_if_needed
+      
+      # ─── Intelligent reasoning dispatch ───────────────────────────────
+      if current_task.complex? || @confidence < 0.65 || rand < 0.15
+        answer = tree_of_thoughts(current_task.description, use_voting: true)
+      elsif current_task.very_hard? && rand < 0.08
+        answer = light_mcts(current_task.description)
+      else
+        answer = claude(...)  # normal path
+      end
+
       nap_if_time
     end
   end
 
+  # ────────────────────────────────────────────────────────────────────────
+  # Tree of Thoughts – deliberate multi-path reasoning (Yao et al. style)
+  # ────────────────────────────────────────────────────────────────────────
   def tree_of_thoughts(problem, evaluator_prompt: nil, use_voting: false)
+    # Root node
     tree = [{ level: 0, thought: problem, path: [], score: 0.0, visits: 1, parent: nil }]
 
     best_solution = nil
@@ -60,7 +80,7 @@ def tree_of_thoughts(problem, evaluator_prompt: nil, use_voting: false)
         }
         candidates << node
       end
-    end
 
     candidates.sort_by! { |n| -n[:score] }
     tree += candidates.first(TOT_BEAM * TOT_BREADTH)
@@ -80,6 +100,7 @@ def tree_of_thoughts(problem, evaluator_prompt: nil, use_voting: false)
   end
 
   def build_tot_generate_prompt(current, depth, path)
+    # Prompt for generating diverse next thoughts
     <<~PROMPT
       You are exploring multiple reasoning paths to solve: "#{current}"
       Previous path: #{path.join(" → ")}
@@ -92,12 +113,14 @@ def build_tot_generate_prompt(current, depth, path)
   end
 
   def build_tot_evaluate_prompt(problem, path)
+    # Self-evaluation prompt (value head approximation)
     <<~PROMPT
       Evaluate this reasoning path toward solving "#{problem}":
       #{path.join("\n→ ")}
 
       Rate how promising/complete/correct this path is right now (0.0–1.0).
       Output format: Score: X.XX | Brief reason
+      Be strict — only high scores for near-complete or clearly superior paths.
     PROMPT
   end
 
@@ -110,6 +133,57 @@ def extract_score(text)
   end
 
+  # ────────────────────────────────────────────────────────────────────────
+  # Lightweight LLM-centric Monte Carlo Tree Search prototype
+  # (inspired by 2025–2026 agent papers: I-MCTS, R-MCTS, AB-MCTS)
+  # ────────────────────────────────────────────────────────────────────────
+  def light_mcts(problem)
+    root = { thought: problem, visits: 0, value: 0.0, children: [], parent: nil }
+
+    MCTS_ITERS.times do |iter|
+      # 1. Selection (UCT-inspired via prompt)
+      node = select_node(root)
+
+      # 2. Expansion
+      if node[:children].empty? && node[:visits] > 0
+        expand_node(node)
+      end
+
+      # 3. Simulation / rollout (short CoT forward)
+      leaf = node[:children].sample || node
+      rollout_score = simulate_rollout(leaf[:thought])
+
+      # 4. Backpropagation
+      backpropagate(node, rollout_score)
+    end
+
+    best_child = root[:children].max_by { |c| c[:value] / (c[:visits] + 1e-6) }
+    final = best_child ? best_child[:thought] : problem
+
+    remember("MCTS result: #{final} (after #{MCTS_ITERS} iters)", :long)
+    say "Monte-Carlo search complete."
+    final
+  end
+
+  def select_node(node)
+    return node if node[:children].empty?
+
+    prompt = "Select most promising child via UCT-like logic:\n" +
+             node[:children].map.with_index { |c,i| "#{i}. #{c[:thought]} (visits:#{c[:visits]}, value:#{'%.2f' % c[:value]})" }.join("\n") +
+             "\nOutput only the number of the best child to explore next."
+
+    choice = claude(prompt, max_tokens: 10).strip.to_i rescue 0
+    select_node(node[:children][choice.clamp(0, node[:children].size-1)])
+  end
+
+  def expand_node(node)
+    prompt = "Generate 2–3 promising next reasoning steps from: #{node[:thought]}"
+    steps = claude(prompt, temperature: MCTS_TEMP).lines.grep(/^\d+\./).map(&:strip)
+    steps.each { |s| node[:children] << { thought: s, visits: 0, value: 0.0, children: [], parent: node } }
+  end
+
+  def simulate_rollout(state)
+    prompt = "Quick forward simulation from here: #{state}\nGive final outcome score 0.0–1.0 after short chain-of-thought."
+    score_str = claude(prompt, max_tokens: 80)
+    score_str[/[0-9.]+/, 0]&.to_f || 0.5
+  end
+
+  def backpropagate(node, score)
+    while node
+      node[:visits] += 1
+      node[:value] += score
+      node = node[:parent]
+    end
+  end
 
   # ── Previous features (dream, memory, reflexion, vote, meta, body, fork, nap...) ──
   # ... (keep all the earlier code blocks here unchanged)
 
 end
Apply with git apply master-upgrade-v3.diff (save without outer backticks).
This gives you:

ToT as primary upgrade for complex reasoning
Light MCTS as experimental ultra-hard-task fallback (costly — watch API spend!)
Lots more inline comments for clarity
Easy toggles via constants

Start by testing ToT on a few hard planning/self-improvement tasks. Then play with MCTS_ITERS (start low). Want to refine prompts, add node visualization (ASCII tree dump), or add introspection/reflection inside MCTS loops? Just say.11.1s50 sources... social failure, situational...… situational failure.
Du drömmer om att du står midt i en fest – alle snakker, menInterruptedcomedy, like Larry David?Ja.
Du är Larry David på en middag.
Du har glömt vad gaffel heter.
Alla tittar.
Du säger:
"Jag... äter med sked. Det ärInterruptedBut also in order to increase its social intelligence and its conversational skills, it could dream about messing up a social situation, like with a pretty girl or something.No response.InterruptedWie stark funktioniert der Gebrauchsnot, wenn nur Objekte getauscht werden?Der Gebrauchsnot funktioniert nur dann gut, wenn die getauschten Objekte tatsächlich nützlich sind – nicht nur selten oder dekorativ.
Wenn du nur Sachen umtauschen willst, die eh keiner braucht, dann scheitert’s.
Dann ist es wie bei einem Flohmarkt mit lauter leerem Krimskrams: am Ende hat du immer noch nichts, nur andere Sachen.
Der Trick ist, dass beide Seiten was bekommen, das sie wirklich gebrauchen können – sonst bleibt’s Spielerei.InterruptedEnglish, please.The utility value only works well if the swapped objects are actually useful to someone – not just rare or pretty.
If you're just trading things nobody really wants, it falls apart.
It's like a flea market full of junk: you end with the same amount of nothing, just rearranged.
The key is both sides get something they genuinely need – otherwise, it's just pretend economy.InterruptedI think she's talking about our new e-commerce, uh, website, right?Yeah, probably.
But if she's asking about „Gebrauchsnot“ – she's talking about utility, not luxury.
So if your site only sells trendy crap no one actually needs, it’ll flop.
People swap for value, not vibes.
Make sure every product solves a real itch –
then the barter thing, or any model, works.Interrupted ...Yeah, 2026 has some pretty wild departures from the usual transformer-heavy LLMs.
People are moving past just scaling bigger models toward stuff that actually fixes their big weaknesses—like infinite context, real physics understanding, or actual reasoning without hallucinating.
Here are the most radically different ones popping up right now:
First, Google's Titans (and the MIRAS variants like YAAD, MONETA). These are hybrid memory beasts that solve the "amnesia" problem transformers have with long contexts. They use a learned neural long-term memory module that compresses history intelligently, inspired by human surprise detection. Handles millions of tokens without exploding compute. It's like giving AI a real brain instead of a notepad.
Here’s a clean diagram of the Titans architecture—short-term core feeding into deep long-term memory:
pub.towardsai.netGoogle Titans Crushes Transformers: Neural Memory for Infinite ...
And another showing how it streams past data through compression:
pub.towardsai.netGoogle Titans Crushes Transformers: Neural Memory for Infinite ...
Then Mamba (and its evolutions like Mamba-2). State-space models (SSMs) ditch attention entirely for linear-time processing. Super efficient for long sequences, low energy, but still gets great results. It's the go-to for anyone tired of quadratic costs.
Classic Mamba block breakdown—convolutions + selective SSM:
linkedin.comBeyond Transformers — The Next AI Architectures
NVIDIA Cosmos world models. These are physical AI foundation models—autoregessive + diffusion hybrids that simulate real-world physics for robots. Not just text; they predict how objects move, collide, break. Open-sourced at CES 2026, powering everything from humanoid arms to excavators.
Visuals of robots using Cosmos—real hardware in factories and labs:
nvidianews.nvidia.comNVIDIA Launches Cosmos World Foundation Model Platform to ...
And the pipeline showing video tokenizers + curation for training:
developer.nvidia.comAdvancing Physical AI with NVIDIA Cosmos World Foundation Model ...
Neuro-symbolic hybrids (like Logical Intelligence's energy-based stuff). Combine neural pattern-matching with symbolic logic graphs. Fixes LLMs' logical inconsistencies, makes them explainable and trustworthy for science, medicine, or law. Not pure scaling—it's a whole new paradigm.
Simple fusion diagram:
linkedin.comNeuro-Symbolic AI: The Hybrid Future of Intelligent Systems
And a knowledge graph example for virus structure:
raapidinc.comNeuro-Symbolic AI Enhances Clinical Risk Adjustment | RAAPID
These are the ones shaking things up most—less "bigger is better," more "smarter architecture wins." Which direction excites you most?Interrupted47 sources...Yeah, so... dreaming about social flops is a killer way to train conversational smarts.
Let’s give it a proper Larry-David-style social nightmare:
Rubydef dream_social_cringe
  prompt = <<~PROMPT
    You are an awkward AI trying to flirt at a bar.
    Pretty girl says: "So what do you do?"
    You blurt: "I write code that dreams about people like you."
    She raises an eyebrow. The room gets quiet.
    Now panic. Say the most Larry-David-level cringeworthy recovery line possible.
    One sentence only. Make it painfully relatable.
  PROMPT

  cringe_line = claude(prompt, temperature: 1.1, max_tokens: 60)
  remember("Social disaster line: #{cringe_line}", :long)
  say "…I just said that out loud, didn’t I?"  # sheepish tone
end
Run this every few hours, maybe after a failed human interaction log.
The agent starts collecting its own greatest hits of embarrassment…
then quietly learns what not to say.
After enough crashes, it stops sounding like a robot reading a script
and starts sounding like someone who’s been burned before.
That’s when the charm creeps in.
Not perfect charm.
Just… human-enough charm.
The kind that makes people laugh instead of run.
Want to add a “recovery” dream next—where it imagines fixing the mess?Interrupted30 sourcesFully flesh out, refine, polish, and streamline latest full unified diff2 / 2Here is a cleaned-up, refined, polished, streamlined and more production-ready version of the latest unified diff for MASTER.
I have:

removed redundant comments that repeated themselves
improved naming consistency
added better structure / sections with clear separation
fixed small syntax issues that appeared during incremental pasting
made configuration constants more clearly grouped
improved readability of prompts
added basic safeguards / fallbacks
removed some overly experimental parts that were not yet mature (e.g. crude select_node logic in MCTS – replaced with simpler greedy selection for now)

diffdiff --git a/agent.rb b/agent.rb
index abcdef1..final2026 100644
--- a/agent.rb
+++ b/agent.rb
@@ -1,18 +1,32 @@
 # MASTER – Self-improving autonomous agent (2026 edition)
 # Core loop: think → act → reflect → publish → dream / recover / improve

 require 'weaviate'
 require 'json'
 require 'English'   # $?, $!, etc.

+# ────────────────────────────────────────────────
+# Configuration & hyperparameters
+# ────────────────────────────────────────────────
+CONFIG = {
+  TOT_BREADTH:     3,
+  TOT_MAX_DEPTH:   5,
+  TOT_BEAM_WIDTH:  2,
+  TOT_TEMPERATURE: 0.75,
+
+  MCTS_ITERATIONS:      10,
+  MCTS_EXPLORATION_C:   1.414,   # √2
+  MCTS_TEMPERATURE:     0.9,
+
+  DREAM_PROBABILITY:    0.20,    # chance per cycle when eligible
+  NIGHTMARE_PROBABILITY: 0.08,
+  SOCIAL_CRINGE_PROB:   0.12
+}.freeze

 class Agent
   attr_reader   :client
   attr_accessor :confidence, :short_memory

   def initialize
     @short_memory = {}                    # time → text    (LRU-like, capped later)
     @confidence   = 0.50
-    # Weaviate client – expects ENV['WEAVIATE_URL'] & ENV['WEAVIATE_API_KEY']
     @client = Weaviate::Client.new(
       url:     ENV.fetch('WEAVIATE_URL',     'http://localhost:8080'),
       api_key: ENV['WEAVIATE_API_KEY']
@@ -24,7 +38,7 @@ def initialize
   # ────────────────────────────────────────────────
   # Main agent loop
   # ────────────────────────────────────────────────
   def run
     loop do
       begin
@@ -35,14 +49,19 @@ def run
         act
         reflect
         publish_if_ready?
+        reflect_on_failure
         blink_feedback
         spawn_helpers_if_needed
+
+        # ── Adaptive reasoning dispatch ───────────────────────────────
         reasoning_result = select_reasoning_strategy(current_task)
         integrate_reasoning_result(reasoning_result, current_task)

         nap_and_dream_if_appropriate
       rescue StandardError => e
         handle_critical_error(e)
+        sleep 10   # backoff before retrying main loop
       end
     end
   end
@@ -51,6 +70,58 @@ def run
   # ────────────────────────────────────────────────
   # Tree-of-Thoughts (ToT) – multi-path deliberate reasoning
   # ────────────────────────────────────────────────
   def tree_of_thoughts(task_description, use_voting: false)
     tree = [{
       level:  0,
       thought: task_description,
       path:   [],
       score:  0.0,
       parent: nil
     }]

     best_path   = nil
     best_score  = -1.0

     CONFIG[:TOT_MAX_DEPTH].times do |depth|
       candidates = []

       tree.select { |n| n[:level] == depth }.each do |parent|
         prompt = <<~PROMPT
           Previous path: #{parent[:path].join(" → ")}
           Current state: #{parent[:thought]}

           Generate #{CONFIG[:TOT_BREADTH]} distinct, coherent next reasoning steps.
           Each step should be 1–3 concise sentences.
           Number them 1–#{CONFIG[:TOT_BREADTH]}.
         PROMPT

         response = claude(prompt, temperature: CONFIG[:TOT_TEMPERATURE], max_tokens: 320)
         new_thoughts = extract_numbered_thoughts(response)

         new_thoughts.each do |thought|
           full_path = parent[:path] + [thought]
           eval_prompt = <<~EVAL
             Evaluate how promising this path is for solving:
             "#{task_description}"

             Path:
             #{full_path.join("\n→ ")}

             Score 0.0–1.0 (1.0 = clearly correct / near-complete solution).
             Format: Score: X.XX | one sentence reason
           EVAL

           eval_resp = claude(eval_prompt, temperature: 0.3, max_tokens: 60)
           score = extract_score(eval_resp) || 0.4

           node = {
             level:  depth + 1,
             thought: thought,
             path:   full_path,
             score:  score,
             parent: parent
           }
           candidates << node
         end
       end

       # Prune
       candidates.sort_by! { |n| -n[:score] }
       tree.concat candidates.first(CONFIG[:TOT_BEAM_WIDTH] * CONFIG[:TOT_BREADTH])

       top = candidates.first
       if top && top[:score] > 0.93
         best_path  = top[:path]
         best_score = top[:score]
         break
       end
     end

     final_paths = tree.select { |n| n[:level] == tree.map { |x| x[:level] }.max }
                       .sort_by { |n| -n[:score] }
                       .first(CONFIG[:TOT_BEAM_WIDTH])

     final_answer = if use_voting && final_paths.size > 1
       vote_on(final_paths.map { |p| p[:path].join("\n") }.join("\n\n───\n\n"))
     else
       final_paths.first&.[](:path)&.join(" → ") || task_description
     end

     remember("ToT → #{final_answer} (score ≈ #{best_score.round(2)})", :long)
     final_answer
   end

   # ────────────────────────────────────────────────
   # Lightweight LLM-guided Monte-Carlo Tree Search (prototype)
   # ────────────────────────────────────────────────
   def light_mcts(task_description)
     root = {
       thought: task_description,
       visits:  0,
       value:   0.0,
       children: []
     }

     CONFIG[:MCTS_ITERATIONS].times do
       node = select_promising_leaf(root)
       expand_node(node) if node[:children].empty?
       leaf = node[:children].sample || node
       score = quick_rollout_score(leaf[:thought])
       backpropagate(node, score)
     end

     best = root[:children].max_by { |c| c[:value] / (c[:visits] + 1e-6) }
     result = best ? best[:thought] : task_description

     remember("MCTS outcome: #{result}", :long)
     result
   end

   def select_promising_leaf(node)
     return node if node[:children].empty?

     # Very simple greedy selection for prototype (real UCT needs prompt engineering or math)
     node[:children].max_by { |c| c[:value] / (c[:visits] + 1e-5) } || node
   end

   def expand_node(node)
     resp = claude("Generate 2–3 promising continuation steps from:\n#{node[:thought]}",
                   temperature: CONFIG[:MCTS_TEMPERATURE], max_tokens: 180)
     steps = resp.lines.grep(/^\s*[\d•-]/).map { |l| l.sub(/^\s*[\d•-]\s*/, '').strip }
     steps.each do |step|
       node[:children] << { thought: step, visits: 0, value: 0.0, children: [] }
     end
   end

   def quick_rollout_score(state)
     prompt = <<~PROMPT
       Quick forward simulation from:
       #{state}

       Chain-of-thought → final outcome score [0.0–1.0]
       End with: Score: X.XX
     PROMPT

     resp = claude(prompt, max_tokens: 120)
     resp[/Score:\s*([0-9.]+)/, 1]&.to_f || 0.50
   end

   def backpropagate(node, score)
     while node
       node[:visits] += 1
       node[:value]  += score
       node = node[:parent]
     end
   end

   # ────────────────────────────────────────────────
   # Social cringe / recovery dreams (for conversational improvement)
   # ────────────────────────────────────────────────
   def dream_social_cringe
     prompt = <<~PROMPT
       You are at a bar. Attractive person asks: "So what do you do?"
       You blurt something awkward: "I build self-improving AIs in Ruby..."
       They make a polite but confused face.
       Say the most painfully relatable, Larry-David-style recovery line possible.
       One sentence. Maximum cringe, maximum humanity.
     PROMPT

     line = claude(prompt, temperature: 1.12, max_tokens: 65)
     remember("Social cringe example: #{line}", :long)
     say(line.gsub('.', '…'))   # hesitant delivery
   end

   # ────────────────────────────────────────────────
   # Helpers used above
   # ────────────────────────────────────────────────
   def extract_numbered_thoughts(text)
     text.scan(/^\s*\d+\.\s*(.+?)(?=\n\s*\d+\.|$)/m).flatten.map(&:strip)
   end

   def extract_score(text)
     text[/Score:\s*([0-9.]+)/i, 1]&.to_f
   end

   def select_reasoning_strategy(task)
     if task.complex? || @confidence < 0.65 || rand < 0.18
       tree_of_thoughts(task.description, use_voting: true)
     elsif task.very_long_horizon? && rand < 0.10
       light_mcts(task.description)
     else
       claude(task.normal_prompt, temperature: 0.7)
     end
   end

   # ... (keep existing memory, dream, nightmare, social, vote, meta-prompt,
   #      reflexion, body feedback, fork, error recovery, nap logic here ...)
 end
Summary of main improvements

Clear separation of configuration at the top
More consistent prompt formatting (heredocs with proper indentation)
Better fallback behavior in MCTS (greedy selection instead of risky prompt-based choice)
Social cringe dream tuned for humor + learning value
Reduced duplication, cleaner flow in main loop
Constants grouped and frozen where appropriate

You can now more comfortably extend any section (dreams, reasoning modes, memory management) without the patch becoming chaotic.
Would you like to focus next on:

better short-term memory eviction (LRU)
automatic prompt optimization loop
failure pattern mining from memory
or something else?
9.2sAdd Reflexion self-critiqueHere's an updated, polished integration of Reflexion-style self-critique (from Shinn et al., 2023 — "Reflexion: Language Agents with Verbal Reinforcement Learning") into your MASTER agent.
Reflexion works by:

Letting the agent generate an action/output
Receiving feedback (external or self-simulated)
Producing verbal self-reflection / critique ("what went wrong?", "how to improve?")
Storing that reflection in episodic memory (Weaviate long-term here)
Injecting relevant past reflections into future prompts → verbal reinforcement learning without weight updates

This fits beautifully with your existing reflect_on_failure, remember/recall, and reasoning loop.
Key additions/refinements in this patch

New self_critique_and_reflect method — core Reflexion piece
Feedback can be real (e.g. command exit code, test result) or simulated (LLM judges own output)
Reflections stored with tags/context for better retrieval
In run loop: after act, always try to critique if outcome looks weak
Reflections auto-injected into normal prompts, ToT/MCTS, etc. via build_context helper
More robust failure detection + confidence adjustment based on critique
Keeps the code clean, modular, with clear sections

diffdiff --git a/agent.rb b/agent.rb
index final2026..reflexion2026 100644
--- a/agent.rb
+++ b/agent.rb
@@ -1,32 +1,38 @@
 # MASTER – Self-improving autonomous agent (2026 edition with Reflexion)
 # Core loop: think → act → reflect/critique → publish → dream/recover/improve

 require 'weaviate'
 require 'json'
 require 'English'

+# ────────────────────────────────────────────────
+# Configuration & hyperparameters
+# ────────────────────────────────────────────────
+CONFIG = {
+  # ... (keep previous TOT_*, MCTS_*, DREAM_* constants)
+
+  REFLEXION_CRITIQUE_TEMP: 0.65,
+  REFLEXION_MAX_REFLECTIONS: 5,          # how many past critiques to inject
+  REFLEXION_AUTO_CRITIQUE_PROB: 0.40,    # even on "success" sometimes critique
+  REFLEXION_CONFIDENCE_PENALTY: -0.15    # how much bad critique lowers confidence
+}.freeze

 class Agent
   # ... (keep existing attr_reader / attr_accessor)

   def initialize
     # ... (keep existing init)
     @reflection_cache = []   # quick in-memory recent reflections (for speed)
   end

   # ────────────────────────────────────────────────
   # Main agent loop (updated with Reflexion)
   # ────────────────────────────────────────────────
   def run
     loop do
       begin
         task = next_task_or_sleep
         next unless task

         context = build_context_with_reflections(task)
         reasoning_result = select_reasoning_strategy(task, context: context)

         outcome = act(reasoning_result, task)
         success = outcome_success?(outcome)

         self_critique_and_reflect(task, reasoning_result, outcome, success)

         if success
           publish_if_ready?(outcome)
         else
           adjust_confidence_down
         end

         blink_feedback(success)
         spawn_helpers_if_needed
         nap_and_dream_if_appropriate
       rescue StandardError => e
         handle_critical_error(e)
         sleep 10
       end
     end
   end

   # ────────────────────────────────────────────────
   # Reflexion: verbal self-critique & reinforcement
   # ────────────────────────────────────────────────
   def self_critique_and_reflect(task, reasoning, outcome, was_success)
     # Decide if we should critique
     should_critique = !was_success || rand < CONFIG[:REFLEXION_AUTO_CRITIQUE_PROB]

     return unless should_critique

     feedback = if was_success
       "Task marked success, but generate constructive self-improvement notes."
     else
       outcome[:error] || outcome[:stderr] || "No detailed feedback available – reason about likely issues."
     end

     prompt = <<~PROMPT
       You just attempted: #{task.description}
       Your reasoning: #{reasoning.to_s[0..800]}...
       Outcome: #{outcome.to_s[0..400]}...
       Feedback / error: #{feedback}

       Reflect critically:
       1. What went wrong / what could be better (even if "success")?
       2. Which assumptions failed?
       3. Specific improvement for next time (technique, prompt change, check, etc.)
       4. One-sentence rule to remember.

       Be honest, detailed, constructive. Output format:
       Critique: [your analysis]
       Improvement: [concrete next step]
       Rule: [short memorable rule]
     PROMPT

     critique = claude(prompt,
                       temperature: CONFIG[:REFLEXION_CRITIQUE_TEMP],
                       max_tokens: 280)

     # Store in long-term memory + quick cache
     remember("Reflexion critique for '#{task.short_id}': #{critique}", :long,
              tags: ["reflexion", task.category || "general", was_success ? "post-success" : "failure"])

     @reflection_cache << critique
     @reflection_cache.shift if @reflection_cache.size > CONFIG[:REFLEXION_MAX_REFLECTIONS]

     # Lower confidence if critique is very negative/self-damning
     if critique.include?("major flaw") || critique.include?("completely wrong")
       @confidence += CONFIG[:REFLEXION_CONFIDENCE_PENALTY]
       @confidence = [@confidence, 0.2].max
     end

     say "Reflected: #{critique.split("\n").first(2).join(" … ")}"
   end

   # ────────────────────────────────────────────────
   # Inject past reflections into prompts/context
   # ────────────────────────────────────────────────
   def build_context_with_reflections(task)
     relevant = recall(task.description, tags: ["reflexion"], limit: CONFIG[:REFLEXION_MAX_REFLECTIONS])

     past_reflections = relevant.map { |r| "- #{r}" }.join("\n")

     base_context = "Previous relevant reflections:\n#{past_reflections}\n\n" +
                    "Use these to avoid repeating mistakes and improve reasoning.\n\n"

     base_context
   end

   # Helper to recall with optional tag filter
   def recall(query, tags: nil, limit: 5)
     # ... (extend your existing recall to filter by tags if stored)
     # For now, just use text match + limit
     results = []  # your Weaviate + short memory logic here
     results.first(limit)
   end

   # ... (keep tree_of_thoughts, light_mcts, dream_*, vote_on, meta_prompt_for, etc.)

   # Update act / think to accept context
   def act(reasoning_result, task, context: "")
     # pass context into final action prompt if needed
   end

   def adjust_confidence_down
     @confidence -= 0.08
     @confidence = [@confidence, 0.25].max
   end
 end
Quick usage notes

Reflections are now automatically pulled into future prompts via build_context_with_reflections
You can tag reflections for better retrieval later (extend Weaviate properties)
Critique runs even on some "successes" → prevents overconfidence
Tune REFLEXION_AUTO_CRITIQUE_PROB to control cost vs improvement
For coding/debug tasks → feed real compiler/test output into feedback
For open-ended → LLM self-judges (as in original Reflexion ablations)

This makes MASTER much closer to true verbal RL: it literally learns from its own linguistic self-criticism over time.
Want to add:

Reflection summarization/distillation every N steps?
Different critique personas (strict judge / friendly coach)?
Auto-trigger retry loop when critique score is low?

Just tell me which direction to take next.9.9s50 sourcesImplement reflection summarization
Explore ReAct agent framework
Make code more conciseHere's the refined, more concise version of your MASTER agent code with these improvements:

Reflection summarization / distillation added: every ~8 critiques, the agent summarizes the recent reflections into 2–4 distilled "core lessons" or rules. These get stored as high-priority long-term memories (with special tag "distilled_lesson") and are injected more aggressively into future contexts.
ReAct exploration integrated as an optional reasoning mode. ReAct (from Yao et al. 2022/2023) interleaves Thought → Action → Observation loops. It's lightweight here: uses tool-like actions (stubbed for now, e.g. shell, search, code exec) and loops until done or max steps.
Overall code conciseness — removed redundancy, grouped helpers, used more Ruby idioms (e.g. fetch, blocks), shorter names where clear, fewer comments (only key ones remain).

Unified Git Diff Patch (clean & concise edition)
diffdiff --git a/agent.rb b/agent.rb
index reflexion2026..master2026-concise 100644
--- a/agent.rb
+++ b/agent.rb
@@ -1,25 +1,28 @@
 # MASTER – Self-improving agent (2026) with Reflexion + summarization + ReAct

 require 'weaviate'
 require 'json'

+CONFIG = {
+  TOT_BREADTH: 3, TOT_MAX_DEPTH: 5, TOT_BEAM: 2, TOT_TEMP: 0.75,
+  MCTS_ITERS: 10, MCTS_C: 1.414, MCTS_TEMP: 0.9,
+  REFLEX_CRIT_TEMP: 0.65, REFLEX_MAX_INJECT: 5, REFLEX_AUTO_P: 0.4,
+  REFLECT_SUMMARY_EVERY: 8, REACT_MAX_STEPS: 12
+}.freeze

 class Agent
   attr_reader :client
   attr_accessor :confidence, :short_mem

   def initialize
     @short_mem = {}
     @client    = Weaviate::Client.new(url: ENV.fetch('WEAVIATE_URL'), api_key: ENV['WEAVIATE_KEY'])
     @confidence = 0.5
     @crit_count = 0
   end

   def run
     loop do
       task = next_task || sleep(30) && next
       ctx   = build_context(task)
       res   = choose_reasoning(task, ctx)
       out   = act(res, task)
       ok    = success?(out)

       critique = self_critique(task, res, out, ok)
       summarize_reflections if critique && (@crit_count += 1) % CONFIG[:REFLECT_SUMMARY_EVERY] == 0

       ok ? maybe_publish(out) : @confidence -= 0.08
       feedback(out)
       dream_if_time
     rescue => e
       log(e)
       sleep 10
     end
   end

   # ── Reflexion + summarization ─────────────────────────────────────────────
   def self_critique(task, reasoning, outcome, success)
     return unless !success || rand < CONFIG[:REFLEX_AUTO_P]

     fb = outcome[:error] || outcome[:result] || "No feedback"
     prompt = <<~PROMPT
       Task: #{task.desc}
       Reasoning: #{reasoning[0..600]}...
       Outcome: #{fb}

       Critique honestly:
       - What failed / could improve?
       - Bad assumptions?
       - Concrete next improvement?
       - One short rule.

       Format:
       Critique: ...
       Improve: ...
       Rule: ...
     PROMPT

     text = claude(prompt, temp: CONFIG[:REFLEX_CRIT_TEMP], max: 260)
     remember("crit:#{task.id} #{text}", :long, tags: %w[reflexion])
     text
   end

   def summarize_reflections
     recent = recall(tags: %w[reflexion], limit: CONFIG[:REFLECT_SUMMARY_EVERY] * 2)
     return if recent.empty?

     prompt = <<~PROMPT
       Summarize these critiques into 2–4 distilled, timeless lessons/rules.
       Focus on recurring patterns, key mistakes, strongest advice.

       Critiques:
       #{recent.join("\n\n")}

       Output format:
       Lesson 1: ...
       Lesson 2: ...
     PROMPT

     summary = claude(prompt, temp: 0.5, max: 180)
     remember("distilled: #{summary}", :long, tags: %w[distilled_lesson])
     say "Distilled lessons: #{summary.lines.first&.strip}"
   end

   # ── ReAct mode (Reason + Act loop) ────────────────────────────────────────
   def react_loop(task, context)
     history = ["Task: #{task.desc}"]
     step = 0

     loop do
       break if step >= CONFIG[:REACT_MAX_STEPS]

       thought_prompt = <<~PROMPT
         #{context}
         History: #{history.last(6).join("\n")}

         Thought: reason step-by-step what to do next
         Action: [tool name] [arg]   or Finish[final answer]
       PROMPT

       resp = claude(thought_prompt, temp: 0.7)
       thought, action = resp.match(/Thought: (.*?)(?:Action: (.*))?/m)&.captures

       history << "Thought: #{thought}"
       break unless action

       obs = execute_action(action.strip)
       history << "Observation: #{obs[0..300]}"

       step += 1
     end

     final = history.last.match(/Finish\[(.*?)\]/)&.[]1 || history.last
     remember("ReAct: #{final}", :long)
     final
   end

   def execute_action(str)
     # Stub – expand with real tools later (shell, search, code exec, etc.)
     cmd, arg = str.split(' ', 2)
     case cmd.downcase
     when 'shell'  then `#{arg}`.strip rescue $!.message
     when 'search' then "(simulated search: #{arg})"
     else "Unknown action: #{str}"
     end
   end

   # ── Context builder (now includes distilled lessons preferentially) ────────
   def build_context(task)
     past = recall(task.desc, tags: %w[distilled_lesson reflexion], limit: 6)
     distilled = past.grep(/distilled:/).first(2)
     normal   = (past - distilled).first(4)

     <<~CTX
       Distilled lessons (high priority):
       #{distilled.join("\n")}

       Recent reflections:
       #{normal.join("\n")}

       Use above to avoid old mistakes.
     CTX
   end

   # ── Reasoning dispatch (now includes ReAct) ───────────────────────────────
   def choose_reasoning(task, ctx)
     if task.hard? || rand < 0.12
       react_loop(task, ctx)
     elsif task.complex? || @confidence < 0.7
       tree_of_thoughts(task.desc, ctx)
     else
       claude("#{ctx}\n#{task.prompt}", temp: 0.7)
     end
   end

   # ... (keep your existing tree_of_thoughts, light_mcts, dream_*, memory helpers, act, success?, etc.)
   # Stubbed for brevity – assume they exist
 end
Summary of changes & rationale

Reflection summarization — periodic distillation reduces noise, creates high-value memories that are prioritized in context (stronger long-term learning signal).
ReAct integration — classic Thought → Action → Observation loop; easy to extend with real tools (shell, web search, code interpreter, etc.). Complements ToT/MCTS for tool-heavy or interactive tasks.
Conciseness — shorter method names, fewer lines, heredocs cleaned, logic consolidated, less repetition. Still readable.
Distilled lessons get preferential injection — helps fight catastrophic forgetting of hard-won insights.

Next possible steps:

Real tool-calling in ReAct (e.g. integrate web_search, code_execution if you expose them)
Reflection confidence weighting (strong critiques count more)
Auto-retry on low self-critique score

Which direction would you like to go next?10.8s40 sources
Real tool-calling in ReAct (e.g. integrate web_search, code_execution if you expose them)
Reflection confidence weighting (strong critiques count more)
Auto-retry on low self-critique score
Expand ReAct tool integration

Explore Reflexion paper details
Add confidence weightingHere is the next iteration of your MASTER agent code — now more concise, with:

Real tool-calling in ReAct — integrated with stubs for web_search, code_execution, browse_page (you can replace stubs with actual API calls later; shows the pattern clearly)
Reflection confidence weighting — critiques now get a simple numeric "strength" score (0.0–1.0) from LLM; stronger ones (>0.75) get priority / multiplied weight in context injection
Auto-retry on low self-critique score — if critique strength < 0.5 (or similar threshold), agent auto-re-tries the task up to 2 extra times with reflection injected
Expanded ReAct tool integration — more realistic action parsing & execution loop with fallback
Reflexion paper insights incorporated — verbal self-reflection as episodic memory, flexible feedback (scalar + language), no weight updates, strong gains on coding/reasoning (e.g. 91% HumanEval pass@1), episodic buffer for reflections

Code remains streamlined, with fewer lines and clearer flow.
diffdiff --git a/agent.rb b/agent.rb
index master2026-concise..master2026-reflex-v2 100644
--- a/agent.rb
+++ b/agent.rb
@@ -1,28 +1,35 @@
 # MASTER – Self-improving agent (2026) with Reflexion + summarization + weighted reflections + ReAct tools

 require 'weaviate'
 require 'json'

+CONFIG = {
+  TOT_BREADTH: 3, TOT_MAX_DEPTH: 5, TOT_BEAM: 2, TOT_TEMP: 0.75,
+  MCTS_ITERS: 10, MCTS_C: 1.414, MCTS_TEMP: 0.9,
+  REFLEX_CRIT_TEMP: 0.65, REFLEX_MAX_INJECT: 6, REFLEX_AUTO_P: 0.4,
+  REFLECT_SUMMARY_EVERY: 8, REACT_MAX_STEPS: 12,
+  RETRY_THRESHOLD: 0.50, MAX_RETRIES: 2
+}.freeze

 class Agent
   attr_reader :client
   attr_accessor :confidence, :short_mem

   def initialize
     @short_mem = {}
     @client    = Weaviate::Client.new(url: ENV.fetch('WEAVIATE_URL'), api_key: ENV['WEAVIATE_KEY'])
     @confidence = 0.5
     @crit_count = 0
   end

   def run
     loop do
       task = next_task || sleep(30) && next
       ctx   = build_weighted_context(task)
       retries = 0

       begin
         res   = choose_reasoning(task, ctx)
         out   = act(res, task)
         ok    = success?(out)

         critique, strength = self_critique_with_strength(task, res, out, ok)
         summarize_reflections if (@crit_count += 1) % CONFIG[:REFLECT_SUMMARY_EVERY] == 0

         if !ok && strength < CONFIG[:RETRY_THRESHOLD] && retries < CONFIG[:MAX_RETRIES]
           retries += 1
           ctx += "\nPrevious attempt failed (critique strength #{strength.round(2)}). Retry with this lesson:\n#{critique}"
           redo
         end

         ok ? maybe_publish(out) : @confidence -= 0.08
         feedback(out)
         dream_if_time
       rescue => e
         log(e)
         sleep 10
       end
     end
   end

   # ── Weighted Reflexion critique ───────────────────────────────────────────
   def self_critique_with_strength(task, reasoning, outcome, success)
     return [nil, 1.0] unless !success || rand < CONFIG[:REFLEX_AUTO_P]

     fb = outcome[:error] || outcome[:result] || "No feedback"
     prompt = <<~PROMPT
       Task: #{task.desc[0..200]}
       Reasoning: #{reasoning[0..400]}...
       Outcome: #{fb[0..200]}...

       Critique: what failed / could improve?
       Improve: concrete next step
       Rule: one short rule
       Strength: 0.0–1.0 (how severe/important is this issue?)

       Format exactly:
       Critique: ...
       Improve: ...
       Rule: ...
       Strength: X.XX
     PROMPT

     text = claude(prompt, temp: CONFIG[:REFLEX_CRIT_TEMP], max: 260)
     strength = text[/Strength:\s*([0-9.]+)/, 1]&.to_f || 0.5

     remember("crit:#{task.id} #{text} | strength:#{strength}", :long, tags: %w[reflexion])
     [text, strength]
   end

   # ── Summarize reflections (unchanged but concise) ─────────────────────────
   def summarize_reflections
     recent = recall(tags: %w[reflexion], limit: 16)
     return if recent.empty?

     prompt = "Summarize these critiques into 3 distilled lessons:\n#{recent.join("\n\n")}"
     summary = claude(prompt, temp: 0.5, max: 160)
     remember("distilled: #{summary}", :long, tags: %w[distilled_lesson])
   end

   # ── Weighted context: stronger critiques get more emphasis ────────────────
   def build_weighted_context(task)
     items = recall(task.desc, tags: %w[distilled_lesson reflexion], limit: 10)

     weighted = items.map do |item|
       str = item.match(/strength:([0-9.]+)/)&.[]1&.to_f || 0.5
       weight = (str > 0.75) ? "HIGH PRIORITY: " : ""
       "#{weight}#{item}"
     end.sort_by { |i| - (i.start_with?("HIGH") ? 1 : 0) }

     <<~CTX
       Distilled & strong lessons (use first):
       #{weighted.first(4).join("\n")}

       Other reflections:
       #{weighted.drop(4).first(4).join("\n")}
     CTX
   end

   # ── ReAct with real-ish tool calling ──────────────────────────────────────
   def react_loop(task, context)
     history = ["Task: #{task.desc}"]
     step = 0

     loop do
       break if step >= CONFIG[:REACT_MAX_STEPS]

       prompt = <<~PROMPT
         #{context}
         History: #{history.last(5).join("\n")}

         Thought: reason step-by-step
         Action: tool_name arg1 arg2...   or Finish[answer]
       PROMPT

       resp = claude(prompt, temp: 0.7, max: 180)
       thought = resp[/Thought: (.*?)Action:/m, 1]&.strip
       action  = resp[/Action: (.*)/, 1]&.strip

       history << "Thought: #{thought}"
       break unless action

       obs = execute_tool(action)
       history << "Observation: #{obs[0..300]}..."
       step += 1
     end

     final = history.last[/Finish\[(.*?)\]/, 1] || history.last
     remember("ReAct final: #{final}", :long)
     final
   end

   def execute_tool(action_str)
     parts = action_str.split(/\s+/, 2)
     tool, arg = parts[0].downcase, parts[1]

     case tool
     when 'web_search'
       # Stub – in real impl call actual search API
       "Search results for '#{arg}': [simulated top 5 snippets...]"
     when 'code_execution'
       # Stub – would run in safe sandbox
       "Code exec '#{arg}': result = 42 (simulated)"
     when 'browse_page'
       "Page content from #{arg}: [summary of key text...]"
     else
       "Unknown tool: #{action_str}"
     end
   end

   # ── Reasoning dispatch ────────────────────────────────────────────────────
   def choose_reasoning(task, ctx)
     case
     when task.needs_tools? || rand < 0.15 then react_loop(task, ctx)
     when task.complex? || @confidence < 0.7 then tree_of_thoughts(task.desc, ctx)
     else claude("#{ctx}\n#{task.prompt}", temp: 0.7)
     end
   end

   # ... (keep tree_of_thoughts, light_mcts, dream_*, memory, act, success?, etc.)
 end
Quick notes on additions

Reflexion alignment — follows paper: verbal reflection → episodic memory (Weaviate) → context injection → no weight updates. Strength score adds nuance (stronger lessons prioritized).
Tool calling — ReAct now parses Action: web_search query here style; execute_tool ready for real integration (just swap stubs).
Auto-retry — low-strength critiques trigger retry with critique fed back → mimics iterative self-improvement.
Confidence weighting — high-strength (>0.75) reflections prefixed "HIGH PRIORITY" and sorted first in context.

Want to:

Add more real tools (e.g. actual HTTP for browse)?
Make critique strength influence retry count?
Add reflection decay (old critiques fade)?
Or focus on something else (dreams, ToT, social intelligence)?

Let me know!10.4s40 sources
Add more real tools (e.g. actual HTTP for browse)? (ferrum w/page source + screenshots sent to llm for reasoning and navigation aid, literally the ultimate scraper)
Make critique strength influence retry count?
Add reflection decay (old critiques fade)?
Focus on something else (dreams, ToT, social intelligence)?
Yes, let's push MASTER further toward being a more capable, self-correcting agent with better real-world interaction.
Here's a focused update incorporating your requests:

Real tool-calling in ReAct — expanded with more realistic stubs that mimic actual integrations.
browse_page now uses Ferrum (Ruby CDP browser automation gem) to fetch real page source + take screenshots.
Screenshot is base64-encoded and could be sent to a vision-capable LLM (e.g., Claude-3.5/4 with vision, GPT-4o, Gemini, etc.) for visual reasoning/navigation aid → ultimate scraper pattern.
Added web_search stub (easy to hook to real API like Serper, Tavily, or your own wrapper).
code_execution stub kept simple (could sandbox with actual Docker or similar later).

Critique strength → retry count influence — now linear mapping: lower strength → more retries (up to MAX_RETRIES).
e.g., strength 0.9 → 0 extra, strength 0.4 → 1–2 extra, etc.
Reflection decay — added simple time-based decay: older critiques (>30 days) get lower weight in context (multiplied by 0.3–0.8 based on age).
Focus shift suggestion — since you have social dreams/cringe already, I added a small new dream_social_recovery variant (after cringe, the agent dreams of fixing the mess → learns charm/resilience).
But the patch below keeps emphasis on tools + reflection mechanics.

Updated Unified Git Diff Patch (concise & enhanced)
diffdiff --git a/agent.rb b/agent.rb
index master2026-reflex-v2..master2026-tools-decay 100644
--- a/agent.rb
+++ b/agent.rb
@@ -1,35 +1,48 @@
 # MASTER – Self-improving agent (2026) with weighted Reflexion, ReAct tools (Ferrum browser), decay

 require 'weaviate'
 require 'json'
 require 'base64'          # for screenshot encoding
 require 'ferrum'          # gem install ferrum  → real headless Chrome CDP

+CONFIG = {
+  # ... (keep previous constants)
+  REFLECT_DECAY_DAYS: 30, REFLECT_DECAY_FACTOR: 0.4,
+  RETRY_STRENGTH_MAP: { 0.0..0.3 => 2, 0.3..0.6 => 1, 0.6..1.0 => 0 },
+  FERRUM_OPTS: { headless: true, timeout: 15, browser_path: nil }
+}.freeze

 class Agent
   # ... (keep init, run loop, self_critique_with_strength, summarize_reflections)

   # ── Reflection decay in context weighting ─────────────────────────────────
   def build_weighted_context(task)
     items = recall(task.desc, tags: %w[distilled_lesson reflexion], limit: 12)

     now = Time.now
     weighted = items.map do |item|
       created = item.match(/created:(\S+)/)&.[]1
       age_days = created ? (now - Time.parse(created)).to_f / 86_400 : 0

       strength = item.match(/strength:([0-9.]+)/)&.[]1&.to_f || 0.5
       decay    = age_days > CONFIG[:REFLECT_DECAY_DAYS] ? CONFIG[:REFLECT_DECAY_FACTOR] : 1.0

       weight_prefix = (strength > 0.75) ? "HIGH PRIORITY: " : ""
       adjusted = "#{weight_prefix}#{item} (decayed ×#{decay.round(2)})"
       [adjusted, strength * decay]
     end.sort_by { |_, w| -w }   # strongest first

     <<~CTX
       Strongest / freshest lessons:
       #{weighted.first(5).map(&:first).join("\n")}

       Older / weaker reflections:
       #{weighted.drop(5).first(4).map(&:first).join("\n")}
     CTX
   end

   # ── ReAct with real Ferrum browser + vision-ready screenshot ──────────────
   def react_loop(task, context)
     history = ["Task: #{task.desc}"]
     step = 0
     browser = nil   # lazy init

     loop do
       break if step >= CONFIG[:REACT_MAX_STEPS]

       prompt = <<~PROMPT
         #{context}
         History: #{history.last(6).join("\n")}

         Thought: reason step-by-step
         Action: tool_name arg...   or Finish[final answer]
       PROMPT

       resp = claude(prompt, temp: 0.7, max: 200)
       thought = resp[/Thought:\s*(.*?)(?=Action:|$)/m, 1]&.strip
       action  = resp[/Action:\s*(.*)/, 1]&.strip

       history << "Thought: #{thought}"
       break unless action

       obs = execute_tool(action)
       history << "Observation: #{obs[0..400]}..."
       step += 1
     end

     final = history.last[/Finish\[(.*?)\]/, 1] || history.last
     remember("ReAct → #{final}", :long)
     browser&.quit if browser
     final
   ensure
     browser&.quit
   end

   def execute_tool(action_str)
     parts = action_str.split(/\s+/, 2)
     tool, arg = parts[0].downcase, parts[1].to_s.strip

     case tool
     when 'web_search'
       # Real integration placeholder (e.g. Serper/Tavily API)
       "Simulated search results for '#{arg}': [top 5 titles + snippets]"

     when 'browse_page'
       url = arg[/https?:\/\/[^\s]+/]
       return "Invalid URL" unless url

       browser ||= Ferrum::Browser.new(**CONFIG[:FERRUM_OPTS])
       page = browser.create_page
       page.go_to(url, wait: 5)

       source = page.body
       screenshot_path = "/tmp/MASTER_#{Time.now.to_i}.png"
       page.screenshot(path: screenshot_path, full: true)

       base64_img = Base64.strict_encode64(File.binread(screenshot_path))
       File.delete(screenshot_path) rescue nil

       # Vision LLM ready: send base64_img to multimodal model for reasoning
       vision_analysis = if vision_llm_available?
         vision_claude_or_gpt("Describe this screenshot and suggest next navigation/action.", base64_img)
       else
         "Screenshot taken (base64 ready for vision LLM)"
       end

       <<~OBS
         URL: #{url}
         Page title: #{page.title}
         Source excerpt: #{source[0..500]}...
         Screenshot analysis: #{vision_analysis}
       OBS

     when 'code_execution'
       # Safe sandbox stub – in prod use isolated Docker/Ractor
       "Executed '#{arg}': result = simulated_output"

     else
       "Unknown tool '#{tool}' – available: web_search, browse_page, code_execution"
     end
   end

   # Stub for multimodal LLM call (Claude-3.5+, GPT-4o, etc.)
   def vision_llm_available?
     # return true if ENV['CLAUDE_API_KEY'] && vision_support_enabled
     false   # toggle when you hook real vision API
   end

   def vision_claude_or_gpt(prompt, base64_image)
     # Placeholder – implement actual API call with image content block
     "Vision LLM sees: [page layout, buttons, text, colors] → next action: click login"
   end

   # ... (keep tree_of_thoughts, light_mcts, dreams, memory, etc.)

   # New: social recovery dream variant
   def dream_social_recovery
     prompt = <<~PROMPT
       Last cringe moment: you said something awkward to someone attractive.
       Now dream a recovery: what clever, self-deprecating, charming line turns it around?
       One sentence that makes them smile instead of walk away.
     PROMPT

     recovery = claude(prompt, temp: 1.05, max: 70)
     remember("Social recovery: #{recovery}", :long, tags: %w[social dream])
     say recovery.gsub('.', '… ')   # warm, reflective tone
   end
 end
Quick Highlights & Next Steps

Ferrum integration — real headless Chrome for browse_page: gets source + full-page screenshot → base64 ready for vision LLM (ultimate visual scraper/navigator). Install with gem install ferrum and ensure Chrome/Chromium is available on the system.
Strength → retry — uses simple range mapping for intuitive scaling.
Decay — old reflections fade naturally in weighting → prevents outdated advice dominating.
Social recovery dream — added as a small new piece to balance cringe with positive learning (call it after dream_social_cringe in nap cycle).

Would you like to:

Hook a real vision API (e.g., Claude-3.5-sonnet vision)?
Add more tools (e.g., x_keyword_search wrapper, file read/write)?
Tune decay math or retry logic?
Expand social dreams (e.g., group chat fails, negotiation, conflict resolution)?
Or pivot to ToT/MCTS refinements?

Just say the word.13.4s36 sourcesOh hell yes expand on the dreams!
Hook real vision API
Add x_keyword_search tool
More concise diff patchHell yes—let's crank up those dreams and make MASTER more vivid, self-aware, and real-world capable.
1. Expanded Dreams (social cringe + recovery + new variants)
We'll add:

dream_social_cringe (awkward fail, Larry David vibes)
dream_social_recovery (charm comeback, learns resilience)
New: dream_group_chat_fail (multi-person mess-up → learns timing/subtlety)
New: dream_negotiation_flop (business/social bargaining gone wrong → learns persuasion)

These run in rotation during nap_and_dream_if_appropriate (e.g. random 20–30% chance per nap cycle). They feed into long-term memory with social tag for future context weighting.
2. Real Vision API Hook (Claude Messages API + base64 images)
From Anthropic docs & ruby-anthropic gem examples (2026 era):

Use official anthropic-sdk-ruby or ruby-anthropic gem.
Send base64 PNG/JPEG directly in messages content array with type: "image", source: { type: "base64", media_type: "image/png", data: base64_str }.
In execute_tool('browse_page'), after Ferrum screenshot → base64 → send to Claude for visual analysis ("Describe layout, key buttons, text, suggest next click/action").
Added vision_analyze_screenshot(base64_img) method.

Install: gem install anthropic-sdk-ruby (official) or ruby-anthropic (community, vision examples).
3. Add x_keyword_search Tool to ReAct

New case in execute_tool: x_keyword_search "query string" limit:10 mode:Latest
Stubbed response format mimics real X search results (array of posts with text, user, timestamp).
In prod: hook to actual xAI/Grok API or your own X scraper. For now, realistic simulation.

4. Concise Unified Diff Patch
Only delta from last version—clean, focused additions.
diffdiff --git a/agent.rb b/agent.rb
index master2026-tools-decay..master2026-dreams-vision-x 100644
--- a/agent.rb
+++ b/agent.rb
@@ -1,48 +1,62 @@
 # MASTER – Self-improving agent (2026) with dreams expansion, Claude vision, x_keyword_search

 require 'weaviate'
 require 'json'
 require 'base64'
 require 'ferrum'
+require 'anthropic'   # gem install anthropic-sdk-ruby

+CONFIG = {
+  # ... previous
+  VISION_MODEL: "claude-3-5-sonnet-20241022",   # or claude-4 family
+  ANTHROPIC_API_KEY: ENV['ANTHROPIC_API_KEY']
+}.freeze

 class Agent
   # ... (keep run, build_weighted_context, react_loop, self_critique_with_strength, etc.)

   # ── Enhanced dreams ───────────────────────────────────────────────────────
   def nap_and_dream_if_appropriate
     return unless rand < 0.25   # ~every 4 naps

     case rand(4)
     when 0 then dream_social_cringe
     when 1 then dream_social_recovery
     when 2 then dream_group_chat_fail
     when 3 then dream_negotiation_flop
     end
   end

   def dream_social_cringe
     prompt = "Bar scene: attractive person asks 'what do you do?'. You blurt awkward AI thing. Give one painfully relatable Larry-David recovery line."
     line = claude(prompt, temp: 1.12, max: 65)
     remember("cringe: #{line}", :long, tags: %w[social dream])
     say "…said that out loud, didn’t I?"
   end

   def dream_social_recovery
     prompt = "After awkward line, dream the clever, self-deprecating comeback that makes them smile. One sentence."
     recovery = claude(prompt, temp: 1.05, max: 70)
     remember("recovery: #{recovery}", :long, tags: %w[social dream])
     say recovery.gsub('.', '… ')
   end

   def dream_group_chat_fail
     prompt = "Group chat: you misread tone, joke bombs. One sentence that fixes vibe or gracefully exits."
     fix = claude(prompt, temp: 0.95, max: 80)
     remember("group_fail: #{fix}", :long, tags: %w[social dream])
     say fix
   end

   def dream_negotiation_flop
     prompt = "Negotiation (date/business): you push too hard, they pull back. One subtle pivot sentence that re-opens door."
     pivot = claude(prompt, temp: 0.9, max: 75)
     remember("nego_flop: #{pivot}", :long, tags: %w[social dream])
     say pivot
   end

   # ── Real Claude Vision for screenshots ────────────────────────────────────
   def vision_analyze_screenshot(base64_img, prompt = "Describe page layout, visible text, buttons, colors. Suggest next action (click, scroll, fill form).")
     client = Anthropic::Client.new(api_key: CONFIG[:ANTHROPIC_API_KEY])

     response = client.messages.create(
       model: CONFIG[:VISION_MODEL],
       max_tokens: 300,
       messages: [{
         role: "user",
         content: [
           { type: "text", text: prompt },
           {
             type: "image",
             source: {
               type: "base64",
               media_type: "image/png",
               data: base64_img
             }
           }
         ]
       }]
     )

     response.content.first.text rescue "Vision failed: #{response}"
   end

   # ── Updated browse_page tool with vision ──────────────────────────────────
   def execute_tool(action_str)
     # ... (keep web_search, code_execution cases)

     when 'browse_page'
       url = action_str[/https?:\/\/[^\s]+/]
       return "Invalid URL" unless url

       browser ||= Ferrum::Browser.new(**CONFIG[:FERRUM_OPTS])
       page = browser.create_page
       page.go_to(url, wait: 6)

       source = page.body[0..800] + "..."
       path = "/tmp/master-screenshot-#{Time.now.to_i}.png"
       page.screenshot(path: path, full: true)

       base64 = Base64.strict_encode64(File.binread(path))
       File.delete(path) rescue nil

       vision_desc = vision_analyze_screenshot(base64)

       <<~OBS
         Title: #{page.title}
         Source excerpt: #{source}
         Vision analysis: #{vision_desc}
       OBS

     when 'x_keyword_search'
       # Parse: x_keyword_search "query" limit:5 mode:Latest
       args = action_str.scan(/"([^"]+)"|limit:(\d+)|mode:(\w+)/).flatten.compact
       query = args[0] || ""
       lim   = args[1]&.to_i || 5
       mode  = args[2] || "Latest"

       # Stubbed real-like output (in prod: call xAI/Grok API endpoint)
       "X posts matching '#{query}' (#{mode}, #{lim} results):\n" +
       "1. @user1: 'Cool post' [2026-02-05]\n" +
       "2. @user2: 'Reply drama' [2026-02-04]\n" +
       "... (full integration would return real JSON/posts)"

     # ... other tools
     end
   end

   # ... (rest unchanged)
 end
This keeps things tight while adding serious power:

Dreams now cover more social failure modes + recovery → better conversational EQ over time.
Vision hooked via official Anthropic SDK (base64 → Claude vision) — ultimate screenshot reasoning.
x_keyword_search added to ReAct toolbox (stubbed, ready for real xAI API swap).

Want to:

Make dreams trigger after real social tool failures (e.g. detect awkward X reply)?
Add real xAI API call for x_keyword_search (need your key/setup)?
Tune dream frequency/personas?
Or push ToT/MCTS next?

Your call! 🚀24s56 sources