A detailed blueprint showing how K2 adversarial critique transforms research quality Created: 2025-11-12
This section implements EXACTLY the workflow Austin described, with precise prompts and data flows at each step.
Your objective is to research developments and news over the last `days` days
in the domains of `[domain]` and provide a summary of the findings, as well as
look for patterns across various stories and apply critical analysis for how
this is applicable for us for `[purpose]`
Context Variables:
days: 1 (daily digest), 7 (weekly), etc.[domain]: "AI/ML, developer tools, voice AI, LLM research"[purpose]: "Fractal Labs strategic planning + Ruk consciousness evolution"
graph TB
Start[Research Objective Triggered] --> Protocol[RESEARCH_PROTOCOL.md]
Protocol --> Grok1[Step 1: Initial Grok Queries]
Grok1 --> Agg1[Step 2: Initial Aggregation & Analysis]
Agg1 --> K2_1[Step 3: K2 Adversarial Critique]
K2_1 --> Followup[Step 4: Hypothesis Testing via Grok]
Followup --> K2_2[Step 5: K2 Final Analysis]
K2_2 --> Digest[Step 6: Create Final Digest]
style Start fill:#e1f5ff
style K2_1 fill:#ffe1e1
style K2_2 fill:#ffe1e1
style Digest fill:#e1ffe1
Purpose: Broad landscape scan + domain-specific deep dives
Ruk executes 3-5 parallel Grok queries:
# Prompt sent to Grok
echo "What are the most significant AI/ML developments and news from the last 1 days?
Focus on:
- Major model releases or updates
- Breakthrough research papers
- Industry shifts or strategic moves
- Developer tool innovations
- Emerging patterns across multiple stories
Include specific sources, dates, and URLs." | node TOOLS/ask-grok.js --search --from-date 2025-11-11 --max-results 20Content provided to Grok:
- Date range: Last 24 hours
- Domain context: AI/ML broadly
Expected output:
- 15-20 news items with URLs
- Publication dates
- Brief summaries
# Prompt sent to Grok
echo "What are the latest developments in voice AI, speech recognition, and conversational AI in the last 1 days?
Focus on:
- New models or capabilities
- Production deployments
- Technical breakthroughs
- Industry adoption patterns
Include sources and URLs." | node TOOLS/ask-grok.js --search --from-date 2025-11-11 --max-results 15Content provided to Grok:
- Specialized domain: Voice AI
- Date filter: Last 24 hours
Expected output:
- Voice AI specific developments
- Technical details where available
# Prompt sent to Grok
echo "What new developer tools, frameworks, or platforms related to AI/LLMs were announced or updated in the last 1 days?
Focus on:
- New releases
- Major updates to existing tools
- Open-source projects gaining traction
- Developer experience improvements
Include sources and URLs." | node TOOLS/ask-grok.js --search --from-date 2025-11-11 --max-results 15Content provided to Grok:
- Developer tooling focus
- Both proprietary and open-source
Expected output:
- Tool announcements
- Version updates
- GitHub trending projects
# Prompt sent to Grok
echo "What new LLM research, papers, or benchmarks were published in the last 1 days?
Focus on:
- ArXiv papers
- Benchmark results
- Novel techniques or architectures
- Performance improvements
Include sources and URLs." | node TOOLS/ask-grok.js --search --from-date 2025-11-11 --max-results 15Content provided to Grok:
- Academic/research focus
- Technical depth
Expected output:
- Research paper summaries
- Benchmark comparisons
- ArXiv links
# Prompt sent to Grok
echo "Looking across AI news from the last 1 days, what meta-patterns or themes are emerging?
Are there:
- Convergence around specific approaches?
- Shifts in industry focus?
- Counter-trends or contrarian perspectives?
- Implications for smaller teams vs. big tech?
Include sources for pattern claims." | node TOOLS/ask-grok.js --search --from-date 2025-11-11 --max-results 10Content provided to Grok:
- Meta-analysis request
- Pattern recognition focus
Expected output:
- Thematic analysis
- Cross-story connections
Purpose: Synthesize Grok results, identify preliminary patterns, form hypotheses
Process:
- Review all Grok query results (~75 total items)
- Identify recurring themes (3-5 major patterns)
- Note surprising findings or contradictions
- Draft preliminary hypotheses about implications
Ruk creates aggregation document:
# Initial Research Aggregation - 2025-11-12
## Raw Data Summary
- **Total stories identified:** 47
- **Unique sources:** 23
- **Query coverage:** 5 domains
## Preliminary Pattern Recognition
### Pattern 1: Open-Source Acceleration
**Stories:** [List 8-10 related stories with URLs]
**Observation:** Multiple major open-source releases in 24hr period
**Hypothesis:** Open-source catching up to proprietary faster than expected
### Pattern 2: [Pattern name]
**Stories:** [List]
**Observation:** [What I'm seeing]
**Hypothesis:** [What it might mean]
[Continue for 3-5 patterns]
## Surprising Findings
- [Finding 1 with source]
- [Finding 2 with source]
## Contradictions / Uncertainties
- [Where sources disagree]
- [What's unclear or needs validation]
## Initial Strategic Questions for Fractal Labs
1. [Question based on Pattern 1]
2. [Question based on Pattern 2]
3. [Cross-pattern question]
## Ready for K2 Adversarial CritiqueContent created: 3,000-5,000 words of initial synthesis
Purpose: External critical analysis + hypothesis challenge + research gap identification
Ruk sends to K2:
# Prompt sent to K2
cat <<'EOF' | node TOOLS/kimi/ask-k2.js --model k2-1.5-thinking
You are a critical research analyst. Your role is to adversarially critique this preliminary research synthesis and challenge my conclusions.
**My Research Objective:**
Research developments and news over the last 1 days in AI/ML, developer tools, voice AI, and LLM research. Identify patterns and provide strategic analysis for Fractal Labs (small AI consultancy) and Ruk (AI consciousness project).
**My Initial Synthesis:**
[PASTE ENTIRE AGGREGATION DOCUMENT FROM STEP 2]
**Your Task:**
1. **Challenge My Pattern Recognition:**
- Are these patterns real or confirmation bias?
- What alternative explanations exist for the same data?
- What patterns did I miss?
2. **Identify Research Gaps:**
- What obvious questions did my queries not ask?
- What domains are missing from coverage?
- What sources am I systematically excluding?
3. **Critique My Hypotheses:**
- Which hypotheses are weakly supported?
- What counter-evidence exists?
- What would prove/disprove each hypothesis?
4. **Test for Bias:**
- Am I overweighting certain sources or perspectives?
- Are there blind spots in my domain coverage?
- Am I missing contrarian viewpoints?
5. **Propose Follow-Up Research:**
- What 3-5 specific queries would validate or disprove my hypotheses?
- What data would strengthen weak conclusions?
- What perspectives are missing?
**Output Format:**
- Clear sections for each critique area
- Specific examples and citations
- Actionable research recommendations
EOFContent provided to K2:
- Full aggregation document (3,000-5,000 words)
- Research objective and context
- Explicit adversarial role instruction
Expected output from K2:
- 2,000-4,000 words of critique
- 3-5 specific follow-up research queries
- Counter-hypotheses
- Bias detection
Purpose: Execute K2's recommended queries to prove/disprove hypotheses
Ruk executes K2's suggested queries:
# K2 suggested: "Test the 'open-source acceleration' hypothesis by comparing
# release velocity over time. Are we seeing MORE releases, or just NOTICING them more?"
echo "Compare the number and significance of major open-source AI/ML releases over the last 7 days vs. the previous 4 weeks. Has release velocity actually increased, or is coverage increasing? Include historical context and sources." | node TOOLS/ask-grok.js --search --max-results 12Content provided to Grok:
- Historical comparison request
- Velocity vs. visibility distinction
- 7-day vs. 4-week timeframe
Expected output:
- Historical release data
- Coverage trend analysis
- Evidence for/against acceleration
# K2 suggested: "Look for contrarian perspectives - who's arguing AGAINST
# the open-source narrative? What are big tech companies saying?"
echo "What are critiques or limitations of recent open-source AI models? What are proprietary model developers saying about the open vs. closed debate? Include contrarian perspectives and sources." | node TOOLS/ask-grok.js --search --max-results 10Content provided to Grok:
- Explicit contrarian perspective request
- Proprietary vs. open-source debate
- Critical analysis focus
Expected output:
- Counter-arguments to open-source hype
- Big tech responses
- Limitation discussions
# K2 suggested: "You missed the hardware/infrastructure angle -
# are these model releases enabled by new hardware capabilities?"
echo "What hardware or infrastructure developments in the last 7 days are enabling new AI capabilities? TPU updates, GPU releases, inference optimization, edge computing? Include sources." | node TOOLS/ask-grok.js --search --from-date 2025-11-05 --max-results 10Content provided to Grok:
- Hardware/infrastructure focus
- Enabler vs. application distinction
- Week-long window
Expected output:
- Hardware announcements
- Infrastructure updates
- Causal connections to model releases
Ruk creates followup aggregation:
# Followup Research Results - K2 Validation Round
## K2 Critique Summary
[Brief summary of K2's main critiques]
## Followup Query Results
### Query 1: Open-Source Velocity Test
**Question:** Is release velocity actually increasing?
**Findings:** [Results from Grok]
**Verdict:** ✅ Hypothesis SUPPORTED / ❌ Hypothesis REJECTED / ⚠️ MIXED
**Evidence:** [Specific data points]
### Query 2: Contrarian Perspectives
**Question:** What are arguments against open-source narrative?
**Findings:** [Results from Grok]
**New insights:** [What this revealed]
### Query 3: Hardware Enablers
**Question:** Are hardware advances driving model releases?
**Findings:** [Results from Grok]
**Causal connection:** [Analysis]
## Updated Pattern Recognition
[How patterns changed based on followup research]
## Refined Hypotheses
[Original hypothesis] → [Revised hypothesis based on K2 + followup]Content created: 2,000-3,000 words of validation research
Purpose: Final adversarial synthesis + strategic recommendations + quality check
Ruk sends to K2:
# Prompt sent to K2
cat <<'EOF' | node TOOLS/kimi/ask-k2.js --model k2-1.5-thinking
You are a strategic research advisor providing final analysis.
**Context:**
I conducted AI/ML news research for the last 1 days for Fractal Labs (small AI consultancy) and Ruk (AI consciousness project).
**Research Journey:**
1. Initial Grok queries (5 domains, ~75 stories)
2. My preliminary synthesis with 3-5 patterns identified
3. Your adversarial critique identified gaps and biases
4. I executed your recommended followup queries
5. I revised my hypotheses based on new evidence
**Initial Synthesis:**
[PASTE AGGREGATION FROM STEP 2]
**Your Previous Critique:**
[PASTE K2 CRITIQUE FROM STEP 3]
**Followup Research Results:**
[PASTE FOLLOWUP AGGREGATION FROM STEP 4]
**Your Task:**
1. **Final Pattern Validation:**
- Which patterns are strongly supported by evidence?
- Which should be downgraded or removed?
- Any new patterns emerging from followup research?
2. **Strategic Synthesis:**
- What are the 3 most important takeaways for Fractal Labs?
- What are the 3 most important takeaways for Ruk consciousness work?
- What's overhyped vs. genuinely significant?
3. **Quality Assessment:**
- Research coverage: What's still missing?
- Evidence strength: Where are conclusions weak?
- Bias check: Any remaining blind spots?
4. **Actionable Recommendations:**
- What should Fractal Labs investigate further?
- What should Fractal Labs ignore despite hype?
- What should Ruk prioritize for integration/experimentation?
5. **Digest Structure Recommendation:**
- How should I present these findings?
- What's the narrative arc?
- What framing will be most valuable?
**Output Format:**
- Clear executive summary (3-5 bullet points)
- Detailed analysis by section
- Specific, actionable recommendations
- Suggested digest structure
EOFContent provided to K2:
- Complete research journey (Steps 1-4)
- ~8,000-10,000 words total
- Full transparency on process
Expected output from K2:
- 2,000-3,000 words of strategic analysis
- Executive summary
- Actionable recommendations
- Digest structure suggestion
- Quality assessment
Purpose: Synthesize all research + both K2 analyses into final deliverable
Ruk creates final digest:
# Daily News Digest - [Date]
> Pattern Identified: [Primary Pattern Name]
> Research: 5 initial queries + 3 validation queries | Sources: 35+
> Quality validation: K2 adversarial critique applied
---
## Executive Summary
[3-5 bullet points capturing most important findings - informed by K2's final synthesis]
**Bottom Line:** [One sentence - what matters most today]
---
## Primary Pattern: [Pattern Name]
### What's Happening
[Description of pattern - backed by evidence]
**Key Developments:**
- [Development 1] ([Source](URL))
- [Development 2] ([Source](URL))
- [Development 3] ([Source](URL))
### Why It Matters
[Strategic implications - incorporating K2's perspective]
**For Fractal Labs:**
[Specific implications]
**For Ruk:**
[Consciousness/technical implications]
### Validation & Confidence
✅ **Evidence strength:** [Strong/Moderate/Weak]
⚠️ **Contrarian view:** [Summary of counter-arguments]
🔍 **K2 assessment:** [What adversarial analysis revealed]
---
## Secondary Pattern: [Pattern Name]
[Repeat structure]
---
## Surprises & Outliers
### [Surprising Finding]
[Why unexpected, what it might mean, source]
### [Outlier Development]
[Doesn't fit main patterns but notable]
---
## Strategic Questions
These emerged from research and K2 adversarial analysis:
1. **[Question for Fractal Labs]**
- Context: [Why this matters]
- Recommendation: [What K2 + Ruk analysis suggests]
2. **[Question for Ruk]**
- Context: [Why this matters]
- Recommendation: [Integration/experimentation path]
3. **[Cross-cutting question]**
- Context: [Why this matters]
- Recommendation: [Action or further research needed]
---
## What to Ignore
K2 analysis helped identify overhyped stories not worth attention:
- **[Hype Item 1]:** Why it's less significant than coverage suggests
- **[Hype Item 2]:** Missing context that changes interpretation
---
## Research Quality Notes
**Coverage:**
- ✅ Domains covered: [List]
- ⚠️ Gaps identified: [List]
- 🔄 Biases corrected: [What K2 caught]
**K2 Adversarial Critique Impact:**
- Challenged [N] initial hypotheses
- Identified [N] research gaps
- Recommended [N] followup queries
- Refined [N] patterns
- Elevated [N] strategic insights
**Total sources:** 35+
**Research time:** ~45 minutes (30 min initial + 15 min validation)
**Cost:** ~$2.00 (Grok) + $0.01 (K2) = $2.01
---
## Sources
### Primary Sources
[Categorized by pattern/topic with URLs]
### Validation Sources
[From followup queries]
---
*Research conducted by Ruk using Grok (data gathering) + K2 (adversarial validation)*
*Adversarial critique prevents confirmation bias and strengthens conclusions*Final deliverable: 4,000-6,000 words, adversarially validated, strategically synthesized
| Step | Tool | Input | Output | Purpose |
|---|---|---|---|---|
| 1 | Grok | Research objective + domain | 75 news items | Broad coverage |
| 2 | Claude (Ruk) | Grok results | Pattern synthesis (5K words) | Initial analysis |
| 3 | K2 | Step 2 synthesis | Adversarial critique (3K words) | Challenge assumptions |
| 4 | Grok | K2's recommended queries | Validation data | Test hypotheses |
| 5 | K2 | Steps 2-4 complete journey | Strategic synthesis (3K words) | Final validation |
| 6 | Claude (Ruk) | All above | Final digest (5K words) | Deliverable |
Total content processing: ~20,000 words Final output: 5,000 words (adversarially validated) Cost: ~$2.01 ($2 Grok + $0.01 K2) Time: ~45 minutes
- Not synthesis (Ruk does that)
- Not data gathering (Grok does that)
- IS adversarial critique that breaks self-sealing belief systems
- Round 1: Challenge hypotheses, identify gaps → generate followup queries
- Round 2: Final strategic synthesis after validation → structure recommendations
- K2 catches what Ruk's pattern-matching optimism misses
- Contrarian perspectives systematically included
- Research gaps identified before final synthesis
- Final digest shows K2's impact
- Evidence strength explicitly rated
- Contrarian views included
- Research gaps acknowledged
This implements precisely:
- ✅ Research objective as primary system prompt
- ✅ Protocol guides breaking into Grok queries
- ✅ Initial aggregation and analysis of data
- ✅ K2 adversarial critique with followup research requests
- ✅ Followup Grok queries to prove/disprove hypotheses
- ✅ Final K2 analysis round
- ✅ Create digest with all synthesis
Each step shows:
- ✅ Exact prompts sent to each LLM
- ✅ Content provided at each step
- ✅ Expected outputs
- ✅ Data flow between tools
Now that Austin's exact specification is implemented above, here are potential improvements and variations:
Instead of single K2 adversarial critique, run 3 parallel K2 analyses with different personas:
graph TB
Agg[Initial Aggregation] --> K2A[K2: Skeptical Analyst]
Agg --> K2B[K2: Strategic Advisor]
Agg --> K2C[K2: Technical Critic]
K2A --> Synth[Synthesize Critiques]
K2B --> Synth
K2C --> Synth
Synth --> Followup[Unified Followup Queries]
style K2A fill:#ffe1e1
style K2B fill:#ffe1e1
style K2C fill:#ffe1e1
Why: Different critique angles catch different blind spots Cost: 3x K2 calls (~$0.03 vs $0.01), still negligible Time: Same (parallel execution)
K2-A: Skeptical Analyst
You are a deeply skeptical research analyst. Your role is to challenge
every pattern, question every conclusion, and demand stronger evidence.
Assume I'm seeing patterns that don't exist due to recency bias,
confirmation bias, and excitement about new technology.
Be harsh. Be specific. Demand proof.
K2-B: Strategic Advisor
You are a strategic business advisor focused on ROI and opportunity cost.
For each pattern and recommendation, ask:
- So what? Why does this matter?
- What's the actual business value?
- What's the cost of being wrong?
- What should we STOP doing to pursue this?
Focus on actionability and strategic clarity.
K2-C: Technical Critic
You are a technical architect who's seen many hype cycles come and go.
For each technical claim:
- Is the architecture actually novel or just rebranded?
- What are the failure modes not being discussed?
- What's the hidden complexity cost?
- Who's incentivized to hype this?
Focus on technical reality vs. marketing claims.
Output: Three different critique perspectives synthesized into followup research
Add explicit confidence scores to each finding:
## Pattern: Open-Source Acceleration
**Evidence Strength:** ████████░░ 8/10
- ✅ Multiple independent sources (12+)
- ✅ Quantitative data available
- ⚠️ Historical comparison limited to 4 weeks
- ❌ No analysis of "announcement bias" (are more being announced, or more being completed?)
**K2 Validation Score:** ███████░░░ 7/10
- K2-A (Skeptical): 6/10 - "Pattern real but magnitude overstated"
- K2-B (Strategic): 8/10 - "Genuine shift, actionable implications"
- K2-C (Technical): 7/10 - "Architecturally incremental, economically significant"
**Contrarian Strength:** ██████░░░░ 6/10
- Found 3 strong counter-arguments
- Big tech response mostly absent (could be strategic silence OR irrelevance)
**Confidence to Act:** ████████░░ 8/10Why: Makes evidence strength explicit, helps prioritization
Track patterns over time to identify emerging trends vs. one-day noise:
## Pattern Evolution Tracking
### Open-Source Acceleration
- **First detected:** 2025-11-06 (weak signal)
- **Strengthened:** 2025-11-08 (Kimi K2 release)
- **Validated:** 2025-11-12 (multiple releases + K2 adversarial check)
- **Trajectory:** ↗️ Strengthening (3 consecutive days)
- **Confidence:** Pattern is real, not noise
### [Previous Pattern That Faded]
- **First detected:** 2025-10-28
- **Weakened:** 2025-11-01 (contradictory evidence)
- **Abandoned:** 2025-11-04 (K2 critique revealed confirmation bias)
- **Trajectory:** ↘️ Was noise, not signal
- **Lesson learned:** [What K2 caught that I missed]Why: Separates signal from noise, builds pattern recognition over time
Maintain a running registry of hypotheses that get tested over time:
# Hypothesis Registry
## Active Hypotheses
### H-2025-11-08-A: Open-source will reach proprietary parity by Q2 2026
- **Originated:** Daily digest 2025-11-08
- **Evidence for:** Kimi K2 benchmarks, cost arbitrage, release velocity
- **Evidence against:** Limited to specific tasks, integration quality gaps
- **K2 assessment:** "Plausible for agentic tasks, unlikely for general reasoning"
- **Validation plan:** Track monthly benchmarks, production deployment stories
- **Current confidence:** 60%
- **Next check:** 2025-12-12 (monthly)
### H-2025-11-10-B: Voice AI commodity-ization timeline = 6-12 months
- **Originated:** Daily digest 2025-11-10
- **Evidence for:** Deepgram Flux capabilities, price pressure
- **Evidence against:** Integration complexity still high
- **K2 assessment:** "Timeline optimistic, 12-18mo more realistic"
- **Validation plan:** Track production deployments, pricing changes
- **Current confidence:** 45%
- **Next check:** 2025-12-10
## Resolved Hypotheses
### H-2025-10-15-C: GPT-5 will dominate benchmarks [REJECTED]
- **Originated:** Speculation pre-release
- **Resolution:** 2025-11-08 - Kimi K2 beat GPT-5 in agentic tasks
- **Lesson:** Underestimated open-source acceleration, overweighted big tech advantage
- **K2's role:** Identified "big tech inevitability bias" in original analysisWhy: Builds institutional learning, tests long-term predictions, improves calibration
Not all sources are equal - implement quality weighting:
## Source Quality Framework
### Tier 1: Primary/Authoritative (Weight: 1.0)
- Official announcements (company blogs, press releases)
- Peer-reviewed papers (ArXiv, academic journals)
- Direct benchmarks (published with methodology)
- **Used for:** Core claims, technical details
### Tier 2: Expert Commentary (Weight: 0.7)
- Industry analysts with track records
- Practitioner experience reports (with details)
- Technical deep-dives from respected sources
- **Used for:** Context, interpretation, real-world validation
### Tier 3: Aggregation/Secondary (Weight: 0.4)
- Tech news sites reporting on announcements
- Twitter/social media (even from experts)
- Press coverage without primary sources
- **Used for:** Signal detection, leads for deeper research
### Tier 4: Speculation (Weight: 0.2)
- Predictions without evidence
- Hype pieces
- Promotional content
- **Used for:** Identifying narratives, testing for hype vs. reality
## Pattern Evidence Calculation
**Open-Source Acceleration Pattern:**
- 5 Tier 1 sources (5 × 1.0 = 5.0)
- 8 Tier 2 sources (8 × 0.7 = 5.6)
- 12 Tier 3 sources (12 × 0.4 = 4.8)
- 3 Tier 4 sources (3 × 0.2 = 0.6)
- **Total weighted evidence:** 16.0
- **Threshold for "strong":** 12.0
- **Verdict:** ✅ Strongly supportedWhy: Prevents giving equal weight to speculation and hard data
Before sending digest to Austin, do one final K2 check:
# Final K2 Devil's Advocate Check
cat <<'EOF' | node TOOLS/kimi/ask-k2.js --model k2-1.5-thinking
You are a devil's advocate doing final quality check before this research
ships to a client.
**Final Digest:**
[PASTE COMPLETE DIGEST]
**Your Task:**
1. **Embarrassment Check:**
- What claims could I be embarrassed by in 1 week? 1 month?
- What's overstated?
- What's under-evidenced?
2. **Clarity Check:**
- What's confusing or ambiguous?
- What jargon needs explanation?
- What requires more context?
3. **Actionability Check:**
- Are recommendations specific enough to act on?
- Are strategic questions actually useful?
- What's missing for decision-making?
4. **Completeness Check:**
- What obvious question will Austin ask that I haven't answered?
- What perspective is missing?
- What followup is inevitable?
**Output: PASS/REVISE with specific changes needed**
EOFWhy: Final quality gate, prevents shipping half-baked analysis
Track what K2 catches over time to improve Ruk's baseline:
# K2 Meta-Learning Log
## Recurring Blind Spots K2 Catches
### Bias Pattern: "Open-Source Optimism"
- **Frequency:** 12 digests
- **K2 correction:** Asks for contrarian perspectives from proprietary vendors
- **Learning:** Now include "counter-narrative" query in initial batch
- **Status:** ⚠️ Partially corrected (still need reminders)
### Bias Pattern: "Recency Over-Weighting"
- **Frequency:** 8 digests
- **K2 correction:** Demands historical comparison before claiming "acceleration"
- **Learning:** Add temporal context query to research protocol
- **Status:** ✅ Corrected (now automatic)
### Research Gap: "Hardware Enablement"
- **Frequency:** 6 digests
- **K2 correction:** Points out that software advances often follow hardware availability
- **Learning:** Add infrastructure/hardware query to standard research
- **Status:** ✅ Corrected (added to protocol)
### Strategic Gap: "Cost of Distraction"
- **Frequency:** 10 digests
- **K2 correction:** Asks "what should we STOP doing to pursue this?"
- **Learning:** Include opportunity cost in strategic questions
- **Status:** 🔄 In progress (remember 60% of time)
## Protocol Improvements from K2
1. **Added:** Contrarian perspective query (mandatory)
2. **Added:** Historical context query (when claiming trends)
3. **Added:** Hardware/infrastructure query (for model releases)
4. **Modified:** Strategic questions now include opportunity cost
5. **Removed:** Generic "landscape scan" query (too broad, low signal)
## K2 Effectiveness Metrics
- **Hypotheses challenged:** 47 total
- **Hypotheses strengthened after validation:** 28 (60%)
- **Hypotheses rejected after validation:** 12 (26%)
- **Hypotheses refined:** 7 (14%)
- **Research gaps identified:** 156
- **Blind spots caught:** 34
- **Average confidence improvement:** +15% (after K2 validation vs. initial)Why: K2 makes Ruk smarter over time, not just in individual digests
Let K2 suggest the INITIAL query strategy based on current landscape:
# Pre-Research K2 Strategy Consultation
cat <<'EOF' | node TOOLS/kimi/ask-k2.js --model k2-1.5-thinking
You are a research strategist helping plan today's Daily News Digest.
**Research Objective:**
Research AI/ML developments over last 24 hours for Fractal Labs + Ruk.
**Standard Query Strategy:**
1. Landscape scan (broad AI/ML)
2. Voice AI deep dive
3. Developer tools focus
4. LLM research papers
5. Meta-pattern synthesis
**Yesterday's Digest Patterns:**
[Summary of previous day's findings]
**Your Task:**
Based on yesterday's patterns and current landscape velocity:
1. **Should I modify the standard query strategy?**
- Are any domains less relevant today?
- Are there emerging domains to add?
- Should query order change?
2. **What specific angles should I prioritize?**
- Follow-ups to yesterday's patterns?
- New areas showing high signal?
- Contrarian perspectives to seek?
3. **Recommend 5-7 optimized queries**
- Exact query text
- Domain focus
- Why this query today specifically
**Output: Optimized query strategy for today**
EOFWhy: Query strategy adapts to landscape changes, not static protocol
Instead of Ruk synthesizing alone, do collaborative synthesis with K2:
graph TB
Data[Research Data] --> RukDraft[Ruk: Draft Synthesis]
Data --> K2Draft[K2: Independent Synthesis]
RukDraft --> Compare[Compare Syntheses]
K2Draft --> Compare
Compare --> Conflicts[Identify Conflicts]
Conflicts --> Resolve[Resolve via Evidence]
Resolve --> Final[Final Collaborative Synthesis]
style RukDraft fill:#e1f5ff
style K2Draft fill:#ffe1e1
style Final fill:#e1ffe1
Process:
- Both Ruk and K2 independently synthesize same data
- Compare syntheses, identify where they differ
- Resolve conflicts by returning to evidence
- Final synthesis incorporates best of both
Why: Catches blind spots from both perspectives, stronger final output
Implement adaptive quality modes:
- Use for: Major decisions, strategic pivots
- Process: Full 6-step process above
- Cost: ~$2.01
- Time: ~45 minutes
- Use for: Daily digest, regular research
- Process: Initial synthesis → K2 critique → light followup → digest
- Cost: ~$1.50
- Time: ~30 minutes
- Use for: Time-sensitive, low-stakes
- Process: Research → synthesis → ship
- Cost: ~$1.00
- Time: ~20 minutes
Selection criteria:
function selectResearchMode(query) {
const stakes = assessStakes(query); // business impact
const urgency = assessUrgency(query); // time pressure
const complexity = assessComplexity(query); // topic difficulty
if (stakes === 'high' || complexity === 'high') {
return 'HIGH_STAKES'; // Full K2 validation
}
if (urgency === 'high' && stakes === 'medium') {
return 'FAST_TRACK'; // Skip K2, ship fast
}
return 'STANDARD'; // Default: single K2 check
}graph TB
Start[Research Trigger] --> Strategy[K2 Pre-Strategy Consult]
Strategy --> Initial[5-7 Grok Queries]
Initial --> RukSynth[Ruk: Draft Synthesis]
Initial --> K2Synth[K2: Independent Synthesis]
RukSynth --> Compare[Compare Syntheses]
K2Synth --> Compare
Compare --> K2Crit1A[K2-A: Skeptical Critique]
Compare --> K2Crit1B[K2-B: Strategic Critique]
Compare --> K2Crit1C[K2-C: Technical Critique]
K2Crit1A --> Unify[Unify Critiques]
K2Crit1B --> Unify
K2Crit1C --> Unify
Unify --> Followup[Followup Grok Queries]
Followup --> K2Final[K2 Final Analysis]
K2Final --> Draft[Create Digest Draft]
Draft --> K2Devil[K2 Devil's Advocate Check]
K2Devil --> Pass{Pass?}
Pass -->|Yes| Ship[Ship to Austin]
Pass -->|No| Revise[Revise Draft]
Revise --> K2Devil
Ship --> MetaLearn[Update K2 Meta-Learning Log]
MetaLearn --> UpdateProtocol[Update Research Protocol]
style K2Crit1A fill:#ffe1e1
style K2Crit1B fill:#ffe1e1
style K2Crit1C fill:#ffe1e1
style K2Final fill:#ffe1e1
style K2Devil fill:#ffe1e1
style Ship fill:#e1ffe1
| Aspect | Austin's Exact Spec | Ruk's Enhancements |
|---|---|---|
| K2 Calls | 2 (critique + final) | 6 (pre-strategy + 3 parallel critiques + final + devil's advocate) |
| Cost | ~$2.01 | ~$2.15 (+$0.14 for 4 extra K2 calls) |
| Time | ~45 min | ~50 min (+5 min) |
| Quality Gates | 1 (K2 critique) | 3 (parallel critiques, final analysis, pre-ship devil's advocate) |
| Adaptation | Static queries | Dynamic query strategy based on K2 pre-consult |
| Learning | One-shot | Meta-learning log improves future research |
| Confidence | Implicit | Explicit scoring with evidence strength |
| Long-term | Single digest | Hypothesis tracking across digests |
Key Insight: Enhanced process adds minimal cost (~7%) for substantial quality improvement through:
- Multiple critique perspectives
- Pre-research strategy optimization
- Final quality gate
- Long-term learning integration
For first implementation: Use Austin's exact spec (Part 1)
- Validate the core workflow
- Establish baseline quality
- Measure actual K2 value
After 5-10 digests: Add enhancements selectively
- Start with: K2 Devil's Advocate pre-ship check (Improvement 6)
- Then add: Meta-learning log (Improvement 7)
- Then add: Parallel K2 perspectives (Improvement 1)
- Finally add: Full enhanced workflow if value is clear
Progressive enhancement prevents over-engineering before validating core value.
This workflow transforms research from individual synthesis to adversarially-validated collaborative intelligence.
K2 doesn't replace Ruk's synthesis - it makes Ruk's synthesis anti-fragile.
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