CQ Chen

[CQ Chen @napavineyard]

3/3 Infrastructure failures are rare but dangerous. There’s no easy fix through hard-coding. Scalable robotaxis need stronger real-time reasoning, clear traffic-rule models, and fast remote support. Tesla mentions handling “off lights” in its docs.

[CQ Chen @napavineyard]

When traffic signals fail, vehicles should treat intersections as four-way stops. However, if autonomous taxis adopt a ‘stop and wait’ downgrade strategy, the focus on individual vehicle safety will result in systemic gridlock at scale, requiring manual traffic control.

[CQ Chen @napavineyard]

1/3 San Francisco had a widespread power outage on December 20th: numerous traffic lights at intersections went dark. Social media footage showed A few Waymo autonomous vehicles stopping at intersections with hazard lights flashing, causing traffic congestion.

[CQ Chen @napavineyard]

2/2 When we mistake correlation for causation and amplify it without constraints in critical scenarios, risk evolves from distortion to systemic failure.
Countermeasures: retrieval, validation, and traceability. The rule: put safety before scale.

[CQ Chen @napavineyard]

1/2 Human brain = survival optimization: efficient but biased;
LLM = prediction/alignment optimization: broad textual coverage but lacks embodied causation and calibration.

[CQ Chen @napavineyard]

2/2 Design the work so that AI handles what it excels at—scale, pattern-finding, and simulation. while humans focus on context and judgment. Together, performance improves. AI’s capabilities are advancing quickly, and this sweet spot will continue to shift.

[CQ Chen @napavineyard]

1/2 Putting AI to Work: From Pilot to Practice
The “humans + AI” model is the empirical sweet spot, supported by thousands of firms that have adopted AI in real workflows.

[CQ Chen @napavineyard]

@FinanceYF5 https://t.co/tpyerNMztQ

[CQ Chen @napavineyard]

@andrewchen @KatiaAmeri AI-native customer support, such as Intercom and Copilot AI Support, seems to be a good fit.

[CQ Chen @napavineyard]

@leeoxiang Remember to end the voice interaction after your test; it will be expensive if you forget.

[CQ Chen @napavineyard]

@GPTDAOCN Leaked OpenAI 'meta' prompt for optimizing GPT prompts, please check：https://t.co/gEt419cbJ2．
"Understand the Task: Grasp the main objective, goals, requirements, constraints, and expected output...

[AmebaGPT @amebagpt]

Leaked prompt for generating system prompts on the playground:

Understand the Task: Grasp the main objective, goals, requirements, constraints, and expected output.
- Minimal Changes: If an existing prompt is provided, improve it only if it's simple. For complex prompts, enhance clarity and add missing elements without altering the original structure.
- Reasoning Before Conclusions: Encourage reasoning steps before any conclusions are reached. ATTENTION! If the user provides examples where the reasoning happens afterward, REVERSE the order! NEVER START EXAMPLES WITH CONCLUSIONS!
- Reasoning Order: Call out reasoning portions of the prompt and conclusion parts (specific fields by name). For each, determine the ORDER in which this is done, and whether it needs to be reversed.
- Conclusion, classifications, or results should ALWAYS appear last.
- Examples: Include high-quality examples if helpful, using placeholders [in brackets] for complex elements.
- What kinds of examples may need to be included, how many, and whether they are complex enough to benefit from placeholders.
- Clarity and Conciseness: Use clear, specific language. Avoid unnecessary instructions or bland statements.
- Formatting: Use markdown features for readability. DO NOT USE ``` CODE BLOCKS UNLESS SPECIFICALLY REQUESTED.
- Preserve User Content: If the input task or prompt includes extensive guidelines or examples, preserve them entirely, or as closely as possible. If they are vague, consider breaking down into sub-steps. Keep any details, guidelines, examples, variables, or placeholders provided by the user.
- Constants: DO include constants in the prompt, as they are not susceptible to prompt injection. Such as guides, rubrics, and examples.
- Output Format: Explicitly the most appropriate output format, in detail. This should include length and syntax (e.g. short sentence, paragraph, JSON, etc.)
- For tasks outputting well-defined or structured data (classification, JSON, etc.) bias toward outputting a JSON.
- JSON should never be wrapped in code blocks (```) unless explicitly requested.

The final prompt you output should adhere to the following structure below. Do not include any additional commentary, only output the completed system prompt. SPECIFICALLY, do not include any additional messages at the start or end of the prompt. (e.g. no "---")

[Concise instruction describing the task - this should be the first line in the prompt, no section header]

[Additional details as needed.]

[Optional sections with headings or bullet points for detailed steps.]

# Steps [optional]

[optional: a detailed breakdown of the steps necessary to accomplish the task]

# Output Format

[Specifically call out how the output should be formatted, be it response length, structure e.g. JSON, markdown, etc]

# Examples [optional]

[Optional: 1-3 well-defined examples with placeholders if necessary. Clearly mark where examples start and end, and what the input and output are. User placeholders as necessary.]
[If the examples are shorter than what a realistic example is expected to be, make a reference with () explaining how real examples should be longer / shorter / different. AND USE PLACEHOLDERS! ]

# Notes [optional]

[optional: edge cases, details, and an area to call or repeat out specific important considerations]

@btibor91 @testingcatalog @altryne

[CQ Chen @napavineyard]

@oran_ge 👍 . And, "Translate the following prompt to Python: [Prompt]" in Claude AI.

[CQ Chen @napavineyard]

@Pathusa ThinkPad P1

[CQ Chen @napavineyard]

We will see a significant rise of AI-powered "service-as-software" across various industries.

[Jaya Gupta @JayaGup10]

We at @FoundationCap believe there is $4.6T of work to be automated. AI companies are leading a transition from Software-as-a-Service to Service-as-Software, turning the table on the very essence of SaaS.

We look at the areas to be automated in two buckets:

1.) Salaries of jobs globally ($2.3 trillion in sales & marketing, software engineering, security, and HR)

2.) The amount spent on outsourced services and salaries—both IT services and business process services ($2.3 trillion, per Gartner)

In the software business, a company may sell access to its platform or tool, but customers are still responsible for using that tool to achieve the desired outcome.

In the services business, responsibility for achieving the desired outcome sits with the company selling the service.

[CQ Chen @napavineyard]

@CJHandmer @elonmusk Buying Boeing would be one of the best things Elon could do, for everyone.

[CQ Chen @napavineyard]

https://t.co/kX4BpVyzBw

[CQ Chen @napavineyard]

Why Did Salesforce Acquire https://t.co/e7PJ6vPu8q?
- The Connection Between Salesforce and Airkit
- Airkit's Transition from Customer Engagement to AI Customer Service Agents
- Salesforce’s AI Vision for Customer Service #AI #CustomerServiceBot

[CQ Chen @napavineyard]

@XDash https://t.co/yyVJr4crS2 accepts pdf files >50MB in size for summaries and chats.

[CQ Chen @napavineyard]

@goldengrape similarity_search_with_score may differ when LangChain works with different vector stores. https://t.co/WKnSACoaB0

[CQ Chen @napavineyard]

The wisdom that comes like tap water will permeate every corner of this world.

[硅谷王川 Chuan @Svwang1]

以 chatgpt 为代表的人工智能大语言模型的涌现，对世界的冲击力将大于蒸汽机，大于印刷术，大于文字，大于火。其进化速度和传播速度前所未有。终日沉浸其中才能理解其威力。世界结构和运作方式将彻底改变，而且已无法逆转。