Anthropic CEO Dario Amodei has forecasted that within 3 to 6 months, AI will generate 90% of all code, and within 12 months, AI might produce nearly all code.. As one should expect, obviously there are skeptics, like Linus Torvalds, who justifiably react that AI is 90% marketing and 10% reality, which in all likelihood, might be about as right as any knowledgeable person who doesn’t have all the eggs in the AI basket can project … EVEN if Torvalds nails it OR that AI is only 5% reality or even a fraction of it, that small percentage is pretty spectacular, and maybe as much as humans can take in … the FACT of the matter about all science, all technology is that even if we conquer the seemingly boundless expanse of the frontiers just ahead of us, there will always be infinitely NEWER frontiers beyond those now new frontiers to explore.

1) Even, if AI tools accelerates productivity of just HALF of the rate that information technology that Moore’s Law did, it still amounts to thousandfold gains every 20 years … which necessarily has to disrupt human ideas about institutions and culture … because, on top of just AI, if you add in other big new things … particular if those other big new things are boosted by smarter, AI-assisted IDEs and dev-extensible open sourcery of info technologies, ie as with turbocharging the competition in DevOps with extensible Linux [especially for K8s] rather than license-constrained Windows or MacOS or old ATT/HP Unix OR Git/GitHub/GitLab rather than Visual Studio Team Edition OR MySQL/MariaDB rather than Oracle or SQL Server OR the Python ecosystem rather than Matlab … well, let’s just say, 2035 might not be recognizable … even if Linus is right about AI. Because of all the OTHER big new things.

2) It’s not just the law of steady, incessant compounding … BUT is the SLOW, steady, sustainable, quasi-predictable compounding that actually what drives economic growth … predictable ROI encourages investment, enabling steady, consistent productivity improvement … whereas the ridiculously overhyped gushing about AI is fine for those IN THE AI INDUSTRY, because the Revolution is happening in them, in their circles of colleagues … but for the REST of the economy, investment in AI and new AI technologies will be driven by more predictable, realistically skeptical practical assessments of what will happen in six months, a year, five years, ten years.

3) If AI is as significant as discovery of fire OR the use of the wheel in transport carts and then mechanisms, well, very few are going to complain after THE FACT … if the actual goods appeared like a magic pony … but the amount gushing overhype is actually detrimental to wider scale adoption because, it’s not just that investors need a degree of predictability to commit capital, ordinary people also need a degree of predictability in order to push on in life … or otherwise, it becomes typical, for those left behind, to state something like “We are so fucked!” and it’s not just them saying that – they actually really believe it. So they will FIGHT adoptions of the technology grumpily at first, then violently, because they believe that they have nothing to lose, ie Luddite reactionary or Communist behavior is rational given the victim mindset of the Luddite reactionary or Communist. The point is astonishing evolutionary growth is fine; there will be winners/losers, but the generally rising tide lifts all boats – revolutionary, radically disruptive growth will spawn revolutions from those who fear being left behind … Win-Lose or Lose-Win scenarios of any kind of game played out in supergames always degrade to Lose-Lose scenarios.

Of course, even before the recent statements about what AI will do, the software development community was justifiably all abuzz about the innately SOCIAL trend that ‘vibe coding represents,’ in which developers or maybe we should say hackers use AI, specifically Large Language Models (LLMs), to generate code from high-level descriptions. This approach is celebrated for its efficiency in creating simple applications and UI designs, enhancing productivity. Of course, there’s skepticism regarding vibe coding effectiveness for complex software projects, where traditional coding skills like system architecture understanding and manual debugging are still deemed essential. Vibe coding is inherently social coding or engagement with other hackers who are also prioritizing the feeling and social atmosphere of a hacker’s digital interface. It’s not only fun, but the early examples are promising. For example, the release of Windsurf Wave 4, with features like Previews and Cascade Auto-Linter, supports this new coding paradigm by facilitating quick UI adjustments and maintaining code quality.

The key is JUST DO IT! That is, of course, what vibe coding or health hacking are completely about. Of course, it’s important to embrace the open-source ethos, prioritize ethical considerations, and foster a collaborative spirit to harness the power of Generative AI for personalized and accessible lifestyle advice … but the key is just get started, make something, fail, learn as much as possible and iterate, but to JUST DO IT!

In order to start getting some practical experience with how vibe coding might work for something PRACTICAL, ie not just a computer game for distractification … but something practical, so that we might begin train ourselves how to think in an AI-assisted world…

HOW would we adapt the gist of the vibe coding paradigm to open source engineering of infrastructure soluionware?**

So, what is *vibe engineering anyway?*

Vibe engineering could be thought of as an AI-assisted mental combination of dialectical bootstrapping with entire frameworks of frameworks, eg think in terms of the Python ecosystem and maybe 40 other similar tool ecoystems/areas of patents on topic of that … resulting dialetical kaliedoscopic storm akin to the old Russian TRIZ/ARIZ theory of inventive problem-solving.

In essence, vibe engineering is about accelerated the negotiations implicit in the contemplation of the results from kaleidoscopic ideation … breaking through the doors of perception doesn’t have to be like dropping acid … it could be thought as being similar to struggling with a problem and then just sleeping on it OR giving a writing assignment your best try and then letting it go cold and coming back to it later with a fresh pair of eyes OR asking a different audience with radically different starting assumptions to consider your idea OR rather than being stuck on one’s own work or the IKEA effect in assembling furniture, instead looking at the global competitive marketplace for hundreds or thousands of better ideas.

In NUTSHELL, the vibe engineering process involves:

Problem Formulation For The Kaliedoscopic TRIZ/ARIZ Of AI

Begin by just talking it out … defining the problem using TRIZ/ARIZ methodology, is basically a spin on Minimal Viable Prototype (MVP) methodology, identifying the primary technical contradiction (e.g., increasing users worsens performance) and the Ideal Final Result (e.g., handling any number of users without degradation). This step ensures clarity and aligns with engineering problem-solving formats, as suggested by the user’s emphasis on concrete, quantifiable objectives.

Initial Solution Generation

Use TRIZ tools like the contradiction matrix and inventive principles to generate initial solution ideas. For example, for scalability issues, principles like “Parameter Change” (e.g., dynamic scaling) or “Segmentation” (e.g., distributing load) could be applied, as seen in software-specific TRIZ applications (TRIZ for software architecture - ScienceDirect).

Incorporating Dialectical Bootstrapping

Encourage contributors to adopt multiple perspectives in their own MVP brainstorming, either individually or as a group. Individually, this means generating conflicting opinions and averaging or in picking the best parts of each of them, as in dialectical bootstrapping. In a group setting, different contributors, as in crowdsourcing, can provide wildly divergent unique viewpoints on what they see as an MVP scenario.

Integration and Synthesis

Collect all proposed MVP solutions and use TRIZ tools to identify and resolve contradictions. For example, if one solution increases server resources and another optimizes code, integrate them by prioritizing based on cost and effectiveness. The user’s 25-step meta-algorithm, such as performing ideality analysis and applying Su-Field analysis, provides a detailed framework for this integration, ensuring a hybrid MVP solution that leverages the best elements.

Use iterative TRIZ/MVP methods like the 9-windows approach to consider the problem at different levels (component, system, user experience) and time frames (current, future). AI can assist by summarizing discussions, identifying patterns, and suggesting TRIZ principles, enhancing efficiency in large, collaborative projects. This aligns with the user’s mention of AI-assisted creative problem-solving, potentially bordering on “hallucinations” for novel ideas.

Community Review and Validation

Present the integrated MVP solution to the open-source community for feedback, ensuring it aligns with project goals, maintainability, and open-source principles. This step addresses the collaborative nature of open-source development, where consensus and contribution management are crucial. The next phase is to launch the MVP prototype and get reactions from beta users/stakeholders.

Challenges and Considerations

Adapting this approach faces challenges, particularly in coordinating diverse contributors and ensuring feasibility in open-source settings. The complexity of TRIZ, requires tailoring and outside-the-box thinkering. TRIZ is about jamming, free of psychological inertia … removing tradition-bound arbitrary limits on the solution space, necessitating techniques like provocation and analogies from distant domains to challenge assumptions … this ideation process is CONFRONTATIONAL, ie everyone’s pet idea and sacred cow will be slaughtered … that’s why it’s so necessary to be free of HATE, free of FEAR.

To overcome these, we try to use the available free communication tools … as best we can … we might start with a GitHub based workflow for managing repositories in the organization, using issue tracking systems to organize tasks, hold regular meetings for engagement, and develop vibe engineering resources. AI tools and things like AI-first IDEs can AI-ify processes and also help to automate the brute-force computation-heavy parts of the process … but it still comes down to doing disciplined HARD engineering. That challenge does not go away with AI or vibe engineering, the nature of the task changes.

More Detailed Steps Of Dialectical Bootstrapping With TRIZ/ARIZ

0. Understanding the Gist of The Vibe Engineering Meta-Algorithm

Vibe engineering is HARD engineering … it’s Human-In-Loop AI-assisted engineering, not really about some magic AI easy button … it’s about using AI as a tool and adopting a meta-cognitive mindset … controlling your mind AND controlling the AI … in order to do AI-assisted engineering … it’s not about wild hallucinations and coloring outside the lines, throwing mud in the direction of where you think the wall is in order see if somehow some of your mud might stick to something … the more that you really UNDERSTAND about how things work, the better that your prompt to the system is going to generate actionable results, which provide the basis for better UNDERSTANDING the big picture, thinkering with resulting ideas and then asking better questions
Get your mind right … purify your vibe .. love, groove, exercise, loosen up, forgive, drive out fear, eliminate hate, don’t rely on external stimulants/atmosphere/music, control your mind, get rid of unhelpful, negative buzzkill vibes as they arise. Be childlike in your joy, excitement and infectious exuberance of HARD ENGINEERING.
In order to optimize the ASSIST offered by LLM AI, you will succeed most rapidly by understanding the algorithms … but you never understand as much as you can learn from your next AI-assisted engineering session … there will always be more to learn and master.
Articulate your prompts in terms of concrete, quantifiable, realistic objectives; your prompts are like code that should make sense to you when you come back to them after forgetting that you wrote them – it’s like good engineering problem-solving format.
Become a student of new developments in meta-cognition topics like dialectical bootstrapping, ARIZ/TRIZ at Level 5 of 6 of Bloom’s Taxonomy or ideally at a level which you could publish in peer-reviewed engineering / scientific journals.

1. Formulate the initial problem statement using standard TRIZ methodology

Identify the single primary technical / physical contradiction
Concretely visualize, then DEFINE the ideal final result (IFR)
State assumptions, factors; specify resources, constraints
Document the code of current mental model compilation/build

2. Generate your first solution approach using traditional TRIZ contradiction matrix

Identify the improving and worsening parameters in the contradiction matrix
Select the most promising inventive principles suggested by the matrix
Apply these principles to develop initial solution concepts
Document your reasoning path and selection criteria

3. Deliberately shift perspective by adopting an opposing worldview

Choose a perspective fundamentally different from your default thinking (e.g., different discipline, industry, or cultural viewpoint)
Temporarily adopt beliefs that contradict your usual assumptions
Consider stakeholders with opposing interests to your primary consideration
Document this alternative mental model explicitly

4. Reformulate the problem statement from this new perspective

Redefine the primary contradiction using terminology from the new perspective
Reframe the ideal final result through this alternative lens
Identify different resources and constraints visible from this viewpoint
Compare how the problem’s importance and urgency differ in this framing

5. Apply TRIZ principles again from this alternative viewpoint

Identify potentially different improving/worsening parameters from this perspective
Select inventive principles that might not have been chosen initially
Apply these principles with the alternative mindset
Document how your solution path differs from the first iteration

6. Identify the contradictions between your two solution approaches

Map explicit conflicts where solutions contradict each other
Identify implicit assumptions that differ between approaches
Note resource allocation differences between solutions
Formulate these meta-contradictions in standard TRIZ format

7. Use these new contradictions as inputs for another round of TRIZ analysis

Apply the contradiction matrix to these meta-contradictions
Identify principles that could resolve conflicts between solutions
Look for patterns in the suggested principles across all iterations
Develop integrative concepts that address these higher-order contradictions

8. Perform an ideality analysis on both solutions

Calculate the ideality ratio for each solution (benefits/(costs+harms))
Identify the unique beneficial functions in each approach
Map how each solution handles harmful functions differently
Create a combined ideality assessment matrix

9. Create a hybrid solution integrating highest-value elements

Rank elements from both solutions by their ideality contribution
Identify synergistic combinations of elements across solutions
Resolve integration conflicts using TRIZ principles
Test the hybrid for unexpected emergent properties or conflicts

10. Apply Su-Field analysis to both solutions

Create substance-field models for each solution approach
Identify different resources utilized in each model
Note differences in interaction types (harmful, insufficient, excessive)
Map the complete field structure differences

11. Integrate the complementary resources identified

Create a unified resource pool from both approaches
Identify novel resource interactions possible with the combined pool
Apply standard Su-Field transformations to the expanded model
Develop resource-optimized hybrid solutions

12. Use the 9-windows thinking tool from different perspectives

Complete the 9-windows grid from your original perspective
Complete a second 9-windows grid from your alternative perspective
Compare differences in system boundaries and hierarchies
Identify integration opportunities across time and system levels

13. Map the psychological inertia revealed by comparing approaches

Identify terminology differences that suggest conceptual biases
Note scope limitations common to both approaches
Identify domain-specific assumptions that limit solution space
Document emotional or intuitive preferences in each approach

14. Systematically eliminate psychological inertia

Apply provocation techniques to assumptions found in both approaches
Use analogies from distant domains to challenge shared limitations
Reframe the problem in language free from discipline-specific jargon
Temporarily adopt impossible or absurd constraints to force novel thinking

15. Apply “smart little people” modeling from different perspectives

Create a micro-level model using entities from your original domain
Develop a second model using entities from your alternative perspective
Analyze different interaction patterns between the models
Identify novel functions visible only at the micro-level

16. Integrate the insights from both sets of modeling

Create a unified micro-model incorporating elements from both approaches
Translate micro-level insights back to macro-level solutions
Identify emergent properties from the combined modeling
Develop implementation concepts based on the integrated model

17. Apply ARIZ algorithm with your original mindset

Follow the complete latest, greatest ARIZ process with your initial assumptions
Document decision points and selection criteria
Note where psychological inertia appears in your analysis
Identify resource limitations in your approach

18. Restart ARIZ with your alternative mindset

Follow latest, greatest ARIZ again with your alternative perspective
Document different choices made at key decision points
Note novel resources identified from this perspective
Identify different contradiction formulations that emerge

19. Compare the mini-problems identified in both ARIZ processes

Create a matrix mapping mini-problems from both approaches
Identify patterns of overlap and divergence
Look for complementary problem decompositions
Develop a unified problem hierarchy incorporating both perspectives

20. Develop a synthesized ideal final result vision

Extract the core functions desired in both IFR formulations
Identify contextual assumptions in each IFR vision
Create an expanded IFR that encompasses both sets of requirements
Develop metrics to evaluate solutions against this integrated IFR

21. Use feature transfer techniques to cross-pollinate concepts

Identify unique features in each solution approach
Apply systematic feature transplantation between solutions
Evaluate compatibilities and conflicts during transfer
Develop adaptation mechanisms for incompatible but valuable features

22. Apply trimming operations to both solutions

Identify elements that can be eliminated in each solution
Determine which functions can be transferred to existing resources
Compare trimming opportunities across both solutions
Develop minimalist integrated solutions based on trimming insights

23. Create a solution evaluation matrix

Develop comprehensive evaluation criteria from both perspectives
Weight criteria based on insights from both approaches
Score solution elements against the weighted criteria
Identify highest-value components across evaluation dimensions

24. Develop a final integrated solution

Combine highest-scoring elements from the evaluation matrix
Resolve remaining contradictions with appropriate TRIZ principles
Verify the solution meets the expanded IFR vision
Create an implementation roadmap with contingencies

25. Document the cognitive patterns revealed by your dialectical process

Map the patterns of divergence between your thought processes
Identify which perspective shifts yielded the most valuable insights
Document specific TRIZ principles that were most effective across perspectives
Create a personalized dialectical template for future problem-solving

Vibe Engineering -- Dialectical Bootstrapping, TRIZ/ARIZ, et al