Stability

[Viewpoint] Large models are inherently nondeterministic; the same question can yield different answers. To make AI reproduce human judgments reliably, deterministic tools (e.g., code) must be used to constrain their output paths.

[Logic chain] Large models generate probabilistically → results drift → products become unusable → wrap them externally with a deterministic tool (e.g., a "harness") → output follows a preset judgment chain → ensuring consistency and usability.

[Failure condition] If large models themselves become fully deterministic and controllable, then no external deterministic tool is needed.

[Related fields] AI applications, technical engineering, decision-making and cognition.

[Viewpoint] The core way to prove service stability to clients is by "disclosing the ingredient list" (making internal processes transparent) and providing real customer cases ("who has drunk it"). The former dispels fears about the production black box, while the latter leverages the herd effect to reduce decision risk.

[Logic Chain] Just as an AD calcium milk factory publicizes its bottling process and formula, clients feel more reassured when they see a clean, controllable process; cases show that others have tried it first without issues, forming proof of safety. The combination of the two turns abstract service reliability into tangible evidence, especially effective for picky clients and large-account procurement.

[Failure Conditions] If the service involves core secrets that cannot be made transparent, or cases involve privacy that cannot be disclosed, alternatives such as qualifications and authoritative endorsements are needed; if the client only pursues occasional explosive results and does not value stability, this approach has limited effect—but such clients are often not stable, major accounts.

[Related Fields] Marketing & Traffic, Sales

Viewpoint: Stability in life does not come from an unchanging environment, but from the ability to dynamically navigate and respond to constant change.

Logic chain: The external environment is always shifting, so static stability is an illusion. Only by improving one's capacity to adapt and engage in strategic interaction can relative stability be maintained.

Failure condition: If the environment remains absolutely stable over the long term (e.g., under a monopolistic welfare system), the advantages of dynamic capability become less apparent.

Related fields: Personal growth, decision-making, and cognition.

Viewpoint: Low-risk products may moderately accept lesser-known brands to balance costs, while high-risk products must prioritize reliability and safety above all.

Logic chain: Failures in low-voltage devices, for instance, have limited consequences, allowing users to opt for lesser-known brands that offer lower prices at the expense of average stability to cut costs. High-power devices, however, can lead to safety incidents once problems occur, with irreversible risks. Therefore, priority must be given to brands with proven quality stability and reliable reputations, leaving no room for侥幸.

Failure conditions: If low-voltage devices are used in critical tasks such as surveillance or security, stability also becomes a requirement; if a lesser-known brand's specific product category has already been validated as safe and stable through market use, it can be considered to have met the reliability standard.

Related fields: Consumption and daily life, decision-making and cognition

Point: For computation tasks that need to run unattended for extended periods, the stability of a development tool is more critical than a missing model's support; gaps in key model ecosystems can severely affect tool selection. Logic chain: During full computations that run for tens of minutes to several hours, a tool crash directly interrupts the task and forces rework, making stability the baseline. At the same time, if a tool does not support the core models that developers rely on, it will hardly be adopted even if it is stable. Failure conditions: If tasks can be chunked and support resuming from checkpoints, the demand for single-run long-term stability decreases; if developers can manually integrate models, the pain of missing support is alleviated. Related fields: Technical engineering, AI applications.

Claim: The instability of AI-assisted coding tools (Vibe Coding) can severely disrupt developer flow, stripping the tools of their efficiency advantage.

Logic chain: Vibe Coding relies on a continuous stream of thought and interaction rhythm. Tool anomalies, disconnections, or inconsistent results can leave developers stuck in intermediate states, forcing them to switch context and reducing overall output.

Failure condition: The claim no longer holds when tools have robust resume and auto-recovery mechanisms, or when developers have a high tolerance for interruptions.

Related fields: Technology Engineering, AI Applications

观点 Some AI products' deep research features lack stability; under certain prompts, they may crash and fail to produce output.

逻辑链 Repeated attempts with the same prompt set consistently caused the feature to freeze, indicating that the feature may have defects with complex or longer contexts, affecting reliability.

失效条件 This occurs only on specific model versions or during specific time periods, and may be fixed later.

关联领域 AI applications

Opinion: Based on real-world usage, Huawei bands significantly outperform Xiaomi in core algorithm accuracy and functional stability, and the gap is widening.

Logic chain: After continuously using both products, the difference is obvious in subjective experience—covering the accuracy of basic metrics like heart rate and sleep tracking, as well as system stability with no lag and low latency—which drives users to switch back to Huawei.

Invalidation condition: If Xiaomi substantially improves its algorithms and stability in future firmware updates, or if Huawei regresses severely, the gap may narrow or even reverse.

Related fields: Wearable devices, consumer electronics.

Viewpoint: When invoking AI models to generate content, setting the temperature parameter too high (e.g., unspecified or exceeding 1.2) may lead to the generation of invalid Unicode characters, potentially causing service crashes. A stable threshold is typically below 1.2.

Logic Chain: High temperature increases output randomness, which may cause the model to produce illegal Unicode sequences that downstream systems cannot handle, resulting in errors. Lowering the temperature can improve output stability and predictability.

Failure Conditions: Certain creative generation tasks may require higher temperatures, but the stability risks must be weighed accordingly.

Related Fields: AI applications, technical engineering

Viewpoint: Continuously refining prompts can significantly improve the first-pass success rate and accuracy of AI image generation, reaching a fully usable level.

Logic chain: Repeatedly adjusting prompts → the model understands the intent more precisely → the probability of generating a satisfactory result on the first attempt increases → manual screening and retries are reduced, making the workflow industrialized and usable.

Failure conditions: Model version updates may invalidate existing prompts; if the target image is too complex or the model's capability is limited, prompt optimization has a ceiling.

Related fields: AI applications, content creation.

Viewpoint: Maintaining long-term pessimism, low desires, and low expectations, and treating one’s own luck as zero, can actually lead to solid, steady effort. People with a risk-averse personality rarely encounter unexpected setbacks.

Logic chain: Extremely low expectations prevent reckless moves; risk aversion leads to conservative decisions; fewer mistakes are made, life and career fluctuate less, and long-term stability is higher.

Failure conditions: In fields that require risk-taking and breakthrough innovation—such as entrepreneurship and scientific research—excessive conservatism can cause missed opportunities.

Related fields: Personal growth, decision-making, and cognition.

Viewpoint: Subscribing to a service that aggregates multiple AI models, such as Poe, can avoid single-service outages and improve day-to-day stability.

Logical chain: When users use different models like Claude and Stable Diffusion at the same time, if OpenAI experiences an outage, the workflow remains unaffected because other models are available.

Failure conditions: This fails if the aggregation platform itself goes down, or if all models that depend on the same cloud infrastructure fail simultaneously.

Related fields: AI applications, technical engineering.

Viewpoint For consumers who seek stable and consistent taste, pour-over coffee is unreliable due to too many human factors, and capsule coffee is a more practical choice.

Logic Chain Pour-over coffee is greatly affected by variables such as water temperature and flow rate, and subjective taste experiences also vary widely; capsule coffee eliminates these variables through a standardized process, providing stable quality.

Invalidation Condition If consumers place greater value on personalized flavor exploration and the experiential pleasure of manual brewing, the standardization of capsule coffee will seem monotonous.

Related Field Consumption and lifestyle.

Viewpoint

Teams should be staffed with members of different seniority levels to form a tiered structure, so as to meet most people’s growth need to explore the boundaries of their abilities and thereby remain stable within 1–2 years.

Logic chain

Employees want work that expands their capabilities. If a team consists entirely of people with the same seniority, it will trigger cutthroat competition and promotion bottlenecks. A mix of high, middle, and low seniority gives each level room to grow and a mentoring role, which is equivalent to trading growth space for stability at the lowest overall cost.

Failure conditions

If a project requires highly specialized expertise and does not need junior staff to grow, such as a short-term task force, or if team members generally prefer stability over growth, the value of tiered staffing will diminish.

Related fields

Management and Teams

Viewpoint: If a production tool upgrade introduces bugs, promptly roll back to a known stable version to avoid hurting development productivity.

Logic chain: A Wrangler upgrade caused D1 functionality to malfunction, showing that the beta version was not stable enough; in the toolchain, stability should be prioritized over new features.

Invalidation condition: If the new version fixes serious security vulnerabilities or provides indispensable functionality, a trade-off is needed.

Related field: Technical engineering.

Viewpoint: When users choose a tool, the primary consideration should be predictable stability, rather than surface-level factors such as feature highlights or open source.

Logic chain: The value of a tool lies in long-term reliability; the steady development of a small team can provide a higher expectation of stability. Data ownership and open source are only part of trust; ultimately, what users need is a commitment to uninterrupted service and no abrupt changes in direction.

Failure conditions: When a tool provides unique, irreplaceable functionality, users are willing to accept instability; or when the use case is short-term and one-off, the weight of stability decreases.

Related areas: software tool selection, product trust, and user decision-making.

Viewpoint: When choosing smart wearable devices, the actual compatibility and stability of core functions matter more than brand or platform ecosystem. Only devices that are stable and reliable will be kept for the long term.

Logic chain: A user found that a Huawei band could not write encrypted access cards, while a Xiaomi band consistently supported this function, so they gave up the Huawei device and switched back to the Xiaomi Mi Band 4. This shows that when a device has a shortcoming in a key everyday function, it will be replaced even if it performs well in other areas.

Invalidation condition: If the encrypted access card function is not a must-have for the user, or if there are alternatives such as a physical access card or phone NFC, then the compatibility difference will not become the deciding factor.

Related fields: Consumption and lifestyle, technology and engineering.

Viewpoint: The core usage scenario for mobile input methods is light input during the gaps between app switches, so they place extremely high demands on cold-start speed, stability, and low system resource usage.

Logic chain: Third-party input methods provide a better experience during continuous input, but most of the time users only invoke the keyboard briefly. Once problems such as the keyboard failing to appear, lag, or being misidentified as handwriting input occur, users are forced to fall back to the native input method with stable performance, even if the native input method has weaker features. This is because mobile utility apps are used in fragmented bursts, so millisecond-level latency and unreliability become magnified.

Failure conditions: If users type continuously in a single app for a long time (for example, writing), input accuracy and dictionary quality will replace the importance of instantaneous stability; or if the manufacturer deeply cooperates with the system to optimize the keyboard pop-up mechanism.

Related fields: Product and operations