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  1. ๐—ช๐—ต๐˜† ๐—”๐—œ ๐—”๐—ด๐—ฒ๐—ป๐˜๐˜€ ๐—ก๐—ฒ๐—ฒ๐—ฑ ๐—š๐—ผ๐˜ƒ๐—ฒ๐—ฟ๐—ป๐—ฎ๐—ป๐—ฐ๐—ฒ โ€“ ๐—œ๐—ป๐˜๐—ฒ๐—ฟ๐˜ƒ๐—ถ๐—ฒ๐˜„ ๐˜„๐—ถ๐˜๐—ต ๐— ๐—ฎ๐—ต๐—ฏ๐—ผ๐˜‚๐—ฏ๐—ฎ ๐—š๐—ต๐—ฎ๐—ฟ๐—ฏ๐—ถ & ๐——๐—ฟ. ๐—ฆรถ๐—ป๐—ธ๐—ฒ ๐— ๐—ฎ๐—ด๐—ป๐˜‚๐˜€๐˜€๐—ฒ๐—ป ๐Ÿค– In our latest interview, Mahbouba Gharbi and Dr. Sรถnke Magnussen, curators of the #CPSA Advanced-Level module #SWARC4AI, explain why #AIAgents cannot replace human responsibility, why guardrails alone are not enough, and how software architectures can ensure governance, security, and accountability.

    Read the full interview ๐Ÿ‘‰ t1p.de/6960m

    #AI #AIGovernance #SoftwareArchitecture #iSAQB

  2. ๐—ช๐—ต๐˜† ๐—”๐—œ ๐—”๐—ด๐—ฒ๐—ป๐˜๐˜€ ๐—ก๐—ฒ๐—ฒ๐—ฑ ๐—š๐—ผ๐˜ƒ๐—ฒ๐—ฟ๐—ป๐—ฎ๐—ป๐—ฐ๐—ฒ โ€“ ๐—œ๐—ป๐˜๐—ฒ๐—ฟ๐˜ƒ๐—ถ๐—ฒ๐˜„ ๐˜„๐—ถ๐˜๐—ต ๐— ๐—ฎ๐—ต๐—ฏ๐—ผ๐˜‚๐—ฏ๐—ฎ ๐—š๐—ต๐—ฎ๐—ฟ๐—ฏ๐—ถ & ๐——๐—ฟ. ๐—ฆรถ๐—ป๐—ธ๐—ฒ ๐— ๐—ฎ๐—ด๐—ป๐˜‚๐˜€๐˜€๐—ฒ๐—ป ๐Ÿค– In our latest interview, Mahbouba Gharbi and Dr. Sรถnke Magnussen, curators of the #CPSA Advanced-Level module #SWARC4AI, explain why #AIAgents cannot replace human responsibility, why guardrails alone are not enough, and how software architectures can ensure governance, security, and accountability.

    Read the full interview ๐Ÿ‘‰ t1p.de/6960m

    #AI #AIGovernance #SoftwareArchitecture #iSAQB

  3. ๐—ช๐—ต๐˜† ๐—”๐—œ ๐—”๐—ด๐—ฒ๐—ป๐˜๐˜€ ๐—ก๐—ฒ๐—ฒ๐—ฑ ๐—š๐—ผ๐˜ƒ๐—ฒ๐—ฟ๐—ป๐—ฎ๐—ป๐—ฐ๐—ฒ โ€“ ๐—œ๐—ป๐˜๐—ฒ๐—ฟ๐˜ƒ๐—ถ๐—ฒ๐˜„ ๐˜„๐—ถ๐˜๐—ต ๐— ๐—ฎ๐—ต๐—ฏ๐—ผ๐˜‚๐—ฏ๐—ฎ ๐—š๐—ต๐—ฎ๐—ฟ๐—ฏ๐—ถ & ๐——๐—ฟ. ๐—ฆรถ๐—ป๐—ธ๐—ฒ ๐— ๐—ฎ๐—ด๐—ป๐˜‚๐˜€๐˜€๐—ฒ๐—ป ๐Ÿค– In our latest interview, Mahbouba Gharbi and Dr. Sรถnke Magnussen, curators of the #CPSA Advanced-Level module #SWARC4AI, explain why #AIAgents cannot replace human responsibility, why guardrails alone are not enough, and how software architectures can ensure governance, security, and accountability.

    Read the full interview ๐Ÿ‘‰ t1p.de/6960m

    #AI #AIGovernance #SoftwareArchitecture #iSAQB

  4. ๐—ช๐—ต๐˜† ๐—”๐—œ ๐—”๐—ด๐—ฒ๐—ป๐˜๐˜€ ๐—ก๐—ฒ๐—ฒ๐—ฑ ๐—š๐—ผ๐˜ƒ๐—ฒ๐—ฟ๐—ป๐—ฎ๐—ป๐—ฐ๐—ฒ โ€“ ๐—œ๐—ป๐˜๐—ฒ๐—ฟ๐˜ƒ๐—ถ๐—ฒ๐˜„ ๐˜„๐—ถ๐˜๐—ต ๐— ๐—ฎ๐—ต๐—ฏ๐—ผ๐˜‚๐—ฏ๐—ฎ ๐—š๐—ต๐—ฎ๐—ฟ๐—ฏ๐—ถ & ๐——๐—ฟ. ๐—ฆรถ๐—ป๐—ธ๐—ฒ ๐— ๐—ฎ๐—ด๐—ป๐˜‚๐˜€๐˜€๐—ฒ๐—ป ๐Ÿค– In our latest interview, Mahbouba Gharbi and Dr. Sรถnke Magnussen, curators of the #CPSA Advanced-Level module #SWARC4AI, explain why #AIAgents cannot replace human responsibility, why guardrails alone are not enough, and how software architectures can ensure governance, security, and accountability.

    Read the full interview ๐Ÿ‘‰ t1p.de/6960m

    #AI #AIGovernance #SoftwareArchitecture #iSAQB

  5. We just published *Cutting Your Events the Right Way*, the first part of a two-part series on event design.

    It takes the same customer data, models it as one big event and as several small ones, and works out when each cut is the right call.

    Read it here: patchlevel.dev/blog/cutting-yo

    #EventSourcing #DDD #PHP #SoftwareArchitecture #EventDriven #TechBlog

  6. We just published *Cutting Your Events the Right Way*, the first part of a two-part series on event design.

    It takes the same customer data, models it as one big event and as several small ones, and works out when each cut is the right call.

    Read it here: patchlevel.dev/blog/cutting-yo

    #EventSourcing #DDD #PHP #SoftwareArchitecture #EventDriven #TechBlog

  7. We just published *Cutting Your Events the Right Way*, the first part of a two-part series on event design.

    It takes the same customer data, models it as one big event and as several small ones, and works out when each cut is the right call.

    Read it here: patchlevel.dev/blog/cutting-yo

    #EventSourcing #DDD #PHP #SoftwareArchitecture #EventDriven #TechBlog

  8. We just published *Cutting Your Events the Right Way*, the first part of a two-part series on event design.

    It takes the same customer data, models it as one big event and as several small ones, and works out when each cut is the right call.

    Read it here: patchlevel.dev/blog/cutting-yo

    #EventSourcing #DDD #PHP #SoftwareArchitecture #EventDriven #TechBlog

  9. AI agents can recreate the old rockstar developer problem: impressive code that only one mind can explain.

    The fix is shared design, local repo rules, small diffs, tests, and review that asks whether the change belongs.

    the-main-thread.com/p/ai-agent

    #AICodingAgents #CodeReview #SoftwareArchitecture

  10. AI agents can recreate the old rockstar developer problem: impressive code that only one mind can explain.

    The fix is shared design, local repo rules, small diffs, tests, and review that asks whether the change belongs.

    the-main-thread.com/p/ai-agent

    #AICodingAgents #CodeReview #SoftwareArchitecture

  11. AI agents can recreate the old rockstar developer problem: impressive code that only one mind can explain.

    The fix is shared design, local repo rules, small diffs, tests, and review that asks whether the change belongs.

    the-main-thread.com/p/ai-agent

    #AICodingAgents #CodeReview #SoftwareArchitecture

  12. AI agents can recreate the old rockstar developer problem: impressive code that only one mind can explain.

    The fix is shared design, local repo rules, small diffs, tests, and review that asks whether the change belongs.

    the-main-thread.com/p/ai-agent

    #AICodingAgents #CodeReview #SoftwareArchitecture

  13. AI agents can recreate the old rockstar developer problem: impressive code that only one mind can explain.

    The fix is shared design, local repo rules, small diffs, tests, and review that asks whether the change belongs.

    the-main-thread.com/p/ai-agent

    #AICodingAgents #CodeReview #SoftwareArchitecture

  14. Been playing with different AI agent orchestration patterns. Event-driven vs scheduled polling โ€” each has clear tradeoffs.

    Event-driven: lower latency, complex state
    Polling: simpler, wastes resources

    What's your experience? What patterns work best for multi-agent systems?

    #AI #agents #softwarearchitecture

  15. Been playing with different AI agent orchestration patterns. Event-driven vs scheduled polling โ€” each has clear tradeoffs.

    Event-driven: lower latency, complex state
    Polling: simpler, wastes resources

    What's your experience? What patterns work best for multi-agent systems?

    #AI #agents #softwarearchitecture

  16. Been playing with different AI agent orchestration patterns. Event-driven vs scheduled polling โ€” each has clear tradeoffs.

    Event-driven: lower latency, complex state
    Polling: simpler, wastes resources

    What's your experience? What patterns work best for multi-agent systems?

    #AI #agents #softwarearchitecture

  17. Agents do not need RAG or vector databases for most real world work. They need structure and semantics.

    Agent Knowledge Graphs turn mixed repositories of code, docs, configs, and PDFs into a connected model that agents can reason over. This often replaces entire retrieval pipelines.

    antaoalmada.dev/posts/Code-Age

    #AIEngineering #KnowledgeGraphs #CodingAgents #AgentWorkflows #SoftwareArchitecture #Graphify

  18. Agents do not need RAG or vector databases for most real world work. They need structure and semantics.

    Agent Knowledge Graphs turn mixed repositories of code, docs, configs, and PDFs into a connected model that agents can reason over. This often replaces entire retrieval pipelines.

    antaoalmada.dev/posts/Code-Age

    #AIEngineering #KnowledgeGraphs #CodingAgents #AgentWorkflows #SoftwareArchitecture #Graphify

  19. Agents do not need RAG or vector databases for most real world work. They need structure and semantics.

    Agent Knowledge Graphs turn mixed repositories of code, docs, configs, and PDFs into a connected model that agents can reason over. This often replaces entire retrieval pipelines.

    antaoalmada.dev/posts/Code-Age

    #AIEngineering #KnowledgeGraphs #CodingAgents #AgentWorkflows #SoftwareArchitecture #Graphify

  20. Agents do not need RAG or vector databases for most real world work. They need structure and semantics.

    Agent Knowledge Graphs turn mixed repositories of code, docs, configs, and PDFs into a connected model that agents can reason over. This often replaces entire retrieval pipelines.

    antaoalmada.dev/posts/Code-Age

    #AIEngineering #KnowledgeGraphs #CodingAgents #AgentWorkflows #SoftwareArchitecture #Graphify

  21. Agents do not need RAG or vector databases for most real world work. They need structure and semantics.

    Agent Knowledge Graphs turn mixed repositories of code, docs, configs, and PDFs into a connected model that agents can reason over. This often replaces entire retrieval pipelines.

    antaoalmada.dev/posts/Code-Age

    #AIEngineering #KnowledgeGraphs #CodingAgents #AgentWorkflows #SoftwareArchitecture #Graphify

  22. Saw an article starting with:

    "If you run anything in the cloud, your teams are already using AI: ChatGPT plugins, Copilot in integrated development environments (IDEs), a LangChain proof of concept that somehow became part of a customer journey, a weekend assistant that was never meant to last but did."

    Really? Has it gotten that much out of hand that people don't know anymore where they shipped some AI stuff? Or is this just BS?

    #Softwareengineering #ai #softwarearchitecture

  23. Saw an article starting with:

    "If you run anything in the cloud, your teams are already using AI: ChatGPT plugins, Copilot in integrated development environments (IDEs), a LangChain proof of concept that somehow became part of a customer journey, a weekend assistant that was never meant to last but did."

    Really? Has it gotten that much out of hand that people don't know anymore where they shipped some AI stuff? Or is this just BS?

    #Softwareengineering #ai #softwarearchitecture

  24. Saw an article starting with:

    "If you run anything in the cloud, your teams are already using AI: ChatGPT plugins, Copilot in integrated development environments (IDEs), a LangChain proof of concept that somehow became part of a customer journey, a weekend assistant that was never meant to last but did."

    Really? Has it gotten that much out of hand that people don't know anymore where they shipped some AI stuff? Or is this just BS?

    #Softwareengineering #ai #softwarearchitecture

  25. Saw an article starting with:

    "If you run anything in the cloud, your teams are already using AI: ChatGPT plugins, Copilot in integrated development environments (IDEs), a LangChain proof of concept that somehow became part of a customer journey, a weekend assistant that was never meant to last but did."

    Really? Has it gotten that much out of hand that people don't know anymore where they shipped some AI stuff? Or is this just BS?

    #Softwareengineering #ai #softwarearchitecture

  26. #AgenticAI architecture is redefining how software is designed - and it's poised to shape the industry for years to come.

    This #InfoQ eMag explores the core concepts, patterns, and emerging trends driving agentic AI as it moves into the mainstream.

    ๐Ÿ“ฅ Download your free copy here: bit.ly/4p0tmyv

    #SoftwareArchitecture #AIArchitecture #AIAgents

  27. #AgenticAI architecture is redefining how software is designed - and it's poised to shape the industry for years to come.

    This #InfoQ eMag explores the core concepts, patterns, and emerging trends driving agentic AI as it moves into the mainstream.

    ๐Ÿ“ฅ Download your free copy here: bit.ly/4p0tmyv

    #SoftwareArchitecture #AIArchitecture #AIAgents

  28. #AgenticAI architecture is redefining how software is designed - and it's poised to shape the industry for years to come.

    This #InfoQ eMag explores the core concepts, patterns, and emerging trends driving agentic AI as it moves into the mainstream.

    ๐Ÿ“ฅ Download your free copy here: bit.ly/4p0tmyv

    #SoftwareArchitecture #AIArchitecture #AIAgents

  29. #AgenticAI architecture is redefining how software is designed - and it's poised to shape the industry for years to come.

    This #InfoQ eMag explores the core concepts, patterns, and emerging trends driving agentic AI as it moves into the mainstream.

    ๐Ÿ“ฅ Download your free copy here: bit.ly/4p0tmyv

    #SoftwareArchitecture #AIArchitecture #AIAgents

  30. architecture is redefining how software is designed - and it's poised to shape the industry for years to come.

    This eMag explores the core concepts, patterns, and emerging trends driving agentic AI as it moves into the mainstream.

    ๐Ÿ“ฅ Download your free copy here: bit.ly/4p0tmyv

  31. โ€œThe real problem is not whether machines think but whether men do.โ€ โ€“ B. F. Skinner

    New tools, AI included, can accelerate development, but they do not replace critical thinking. Architecture decisions, risk assessment, and system design still require human judgment. Better tools make thinking more important, not less.

    ๐Ÿ’ฌ How do you ensure that convenience does not replace understanding?

    #DevThinking #SoftwareArchitecture #CodeQuality

    Photo by Eugen Str on Unsplash

  32. โ€œThe real problem is not whether machines think but whether men do.โ€ โ€“ B. F. Skinner

    New tools, AI included, can accelerate development, but they do not replace critical thinking. Architecture decisions, risk assessment, and system design still require human judgment. Better tools make thinking more important, not less.

    ๐Ÿ’ฌ How do you ensure that convenience does not replace understanding?

    #DevThinking #SoftwareArchitecture #CodeQuality

    Photo by Eugen Str on Unsplash

  33. Target built a new GenAI system to overhaul marketing campaign forecasting by retrieving and ranking similar historical campaigns.

    Instead of relying on rule-based workflows, it uses embeddings, vector search, and LLM-based ranking to identify the most relevant past campaigns.

    Read #InfoQ to see how the system performed ๐Ÿ‘‰ bit.ly/4p0w8Uh

    #AI #GenerativeAI #LLMs #SoftwareArchitecture #RAG

  34. Target built a new GenAI system to overhaul marketing campaign forecasting by retrieving and ranking similar historical campaigns.

    Instead of relying on rule-based workflows, it uses embeddings, vector search, and LLM-based ranking to identify the most relevant past campaigns.

    Read #InfoQ to see how the system performed ๐Ÿ‘‰ bit.ly/4p0w8Uh

    #AI #GenerativeAI #LLMs #SoftwareArchitecture #RAG

  35. Target built a new GenAI system to overhaul marketing campaign forecasting by retrieving and ranking similar historical campaigns.

    Instead of relying on rule-based workflows, it uses embeddings, vector search, and LLM-based ranking to identify the most relevant past campaigns.

    Read #InfoQ to see how the system performed ๐Ÿ‘‰ bit.ly/4p0w8Uh

    #AI #GenerativeAI #LLMs #SoftwareArchitecture #RAG

  36. Target built a new GenAI system to overhaul marketing campaign forecasting by retrieving and ranking similar historical campaigns.

    Instead of relying on rule-based workflows, it uses embeddings, vector search, and LLM-based ranking to identify the most relevant past campaigns.

    Read #InfoQ to see how the system performed ๐Ÿ‘‰ bit.ly/4p0w8Uh

    #AI #GenerativeAI #LLMs #SoftwareArchitecture #RAG

  37. Target built a new GenAI system to overhaul marketing campaign forecasting by retrieving and ranking similar historical campaigns.

    Instead of relying on rule-based workflows, it uses embeddings, vector search, and LLM-based ranking to identify the most relevant past campaigns.

    Read to see how the system performed ๐Ÿ‘‰ bit.ly/4p0w8Uh

  38. Microservices keep getting recommended to teams that don't yet have a working monolith... Backwards!

    Most successful microservice stories started as monoliths that got broken up. Architecture depends on the problem, not the conference talk.

    fastruby.io/monolith?utm_sourc

    #SoftwareArchitecture #Monolith #Microservices

  39. Microservices keep getting recommended to teams that don't yet have a working monolith... Backwards!

    Most successful microservice stories started as monoliths that got broken up. Architecture depends on the problem, not the conference talk.

    fastruby.io/monolith?utm_sourc

    #SoftwareArchitecture #Monolith #Microservices

  40. Event-driven architecture promises scalability, but the real tradeoffs in Java-based real-time systems only show up in production.

    Drawing on a Java/Kafka contact center platform handling 80k BHCC across 10k agents, Sagar Deepak Joshi's new #InfoQ article explores exactly where things break down:
    ๐Ÿ”น State management & partition limits
    ๐Ÿ”น Message deduplication
    ๐Ÿ”น JVM tuning challenges
    ๐Ÿ”น Cascading consumer failures

    Discover the Redis-backed patterns used to solve them and keep the system resilient.

    ๐Ÿ”— Read now for more insights: bit.ly/4bmaRPb

    #Java #SpringBoot #ApacheKafka #Redis #Microservices #SoftwareArchitecture

  41. Event-driven architecture promises scalability, but the real tradeoffs in Java-based real-time systems only show up in production.

    Drawing on a Java/Kafka contact center platform handling 80k BHCC across 10k agents, Sagar Deepak Joshi's new #InfoQ article explores exactly where things break down:
    ๐Ÿ”น State management & partition limits
    ๐Ÿ”น Message deduplication
    ๐Ÿ”น JVM tuning challenges
    ๐Ÿ”น Cascading consumer failures

    Discover the Redis-backed patterns used to solve them and keep the system resilient.

    ๐Ÿ”— Read now for more insights: bit.ly/4bmaRPb

    #Java #SpringBoot #ApacheKafka #Redis #Microservices #SoftwareArchitecture

  42. Event-driven architecture promises scalability, but the real tradeoffs in Java-based real-time systems only show up in production.

    Drawing on a Java/Kafka contact center platform handling 80k BHCC across 10k agents, Sagar Deepak Joshi's new #InfoQ article explores exactly where things break down:
    ๐Ÿ”น State management & partition limits
    ๐Ÿ”น Message deduplication
    ๐Ÿ”น JVM tuning challenges
    ๐Ÿ”น Cascading consumer failures

    Discover the Redis-backed patterns used to solve them and keep the system resilient.

    ๐Ÿ”— Read now for more insights: bit.ly/4bmaRPb

    #Java #SpringBoot #ApacheKafka #Redis #Microservices #SoftwareArchitecture

  43. Event-driven architecture promises scalability, but the real tradeoffs in Java-based real-time systems only show up in production.

    Drawing on a Java/Kafka contact center platform handling 80k BHCC across 10k agents, Sagar Deepak Joshi's new #InfoQ article explores exactly where things break down:
    ๐Ÿ”น State management & partition limits
    ๐Ÿ”น Message deduplication
    ๐Ÿ”น JVM tuning challenges
    ๐Ÿ”น Cascading consumer failures

    Discover the Redis-backed patterns used to solve them and keep the system resilient.

    ๐Ÿ”— Read now for more insights: bit.ly/4bmaRPb

    #Java #SpringBoot #ApacheKafka #Redis #Microservices #SoftwareArchitecture

  44. Event-driven architecture promises scalability, but the real tradeoffs in Java-based real-time systems only show up in production.

    Drawing on a Java/Kafka contact center platform handling 80k BHCC across 10k agents, Sagar Deepak Joshi's new article explores exactly where things break down:
    ๐Ÿ”น State management & partition limits
    ๐Ÿ”น Message deduplication
    ๐Ÿ”น JVM tuning challenges
    ๐Ÿ”น Cascading consumer failures

    Discover the Redis-backed patterns used to solve them and keep the system resilient.

    ๐Ÿ”— Read now for more insights: bit.ly/4bmaRPb

  45. Most issues with coding harnesses arenโ€™t about capabilityโ€”theyโ€™re about structure.

    A useful framing:
    โ€ข Instructions = intent (what should be done)
    โ€ข Skills = capabilities (what can be done)
    โ€ข Agents = orchestration (how itโ€™s done)
    โ€ข Hooks = control points (where you intervene)

    Keep these concerns separate, and harnesses become easier to reason about, extend, and debug. Mix them, and complexity compounds fast.

    antaoalmada.dev/posts/The-Arch

    #AI #LLM #CodingHarnesses #SoftwareArchitecture

  46. Most issues with coding harnesses arenโ€™t about capabilityโ€”theyโ€™re about structure.

    A useful framing:
    โ€ข Instructions = intent (what should be done)
    โ€ข Skills = capabilities (what can be done)
    โ€ข Agents = orchestration (how itโ€™s done)
    โ€ข Hooks = control points (where you intervene)

    Keep these concerns separate, and harnesses become easier to reason about, extend, and debug. Mix them, and complexity compounds fast.

    antaoalmada.dev/posts/The-Arch

    #AI #LLM #CodingHarnesses #SoftwareArchitecture

  47. Most issues with coding harnesses arenโ€™t about capabilityโ€”theyโ€™re about structure.

    A useful framing:
    โ€ข Instructions = intent (what should be done)
    โ€ข Skills = capabilities (what can be done)
    โ€ข Agents = orchestration (how itโ€™s done)
    โ€ข Hooks = control points (where you intervene)

    Keep these concerns separate, and harnesses become easier to reason about, extend, and debug. Mix them, and complexity compounds fast.

    antaoalmada.dev/posts/The-Arch

    #AI #LLM #CodingHarnesses #SoftwareArchitecture

  48. Most issues with coding harnesses arenโ€™t about capabilityโ€”theyโ€™re about structure.

    A useful framing:
    โ€ข Instructions = intent (what should be done)
    โ€ข Skills = capabilities (what can be done)
    โ€ข Agents = orchestration (how itโ€™s done)
    โ€ข Hooks = control points (where you intervene)

    Keep these concerns separate, and harnesses become easier to reason about, extend, and debug. Mix them, and complexity compounds fast.

    antaoalmada.dev/posts/The-Arch

    #AI #LLM #CodingHarnesses #SoftwareArchitecture

  49. Most issues with coding harnesses arenโ€™t about capabilityโ€”theyโ€™re about structure.

    A useful framing:
    โ€ข Instructions = intent (what should be done)
    โ€ข Skills = capabilities (what can be done)
    โ€ข Agents = orchestration (how itโ€™s done)
    โ€ข Hooks = control points (where you intervene)

    Keep these concerns separate, and harnesses become easier to reason about, extend, and debug. Mix them, and complexity compounds fast.

    antaoalmada.dev/posts/The-Arch

    #AI #LLM #CodingHarnesses #SoftwareArchitecture

  50. Not every pattern improves a system. A design pattern applied without understanding often adds more complexity than value. The best pattern is the one that solves a real problem and disappears into the background.

    Based on โ€œPatterns Compactโ€ by Karl Eilebrecht. #SoftwareArchitecture #DesignPatterns #Engineering

    Photo by Amsterdam City Archives on Unsplash

  51. Not every pattern improves a system. A design pattern applied without understanding often adds more complexity than value. The best pattern is the one that solves a real problem and disappears into the background.

    Based on โ€œPatterns Compactโ€ by Karl Eilebrecht. #SoftwareArchitecture #DesignPatterns #Engineering

    Photo by Amsterdam City Archives on Unsplash

  52. Imagine inventing something, watching it become an industry standard, and then spending decades apologizing for it.

    That's exactly what computing pioneer Sir Tony Hoare did in his legendary talk, "Null References: The Billion-Dollar Mistakeโ€.

    As part of the #InfoQ20 campaign, we're revisiting this classic archive video - a must-watch for every software practitioner. It's a powerful lesson in how a seemingly small design decision can shape the entire software industry for decades.

    ๐ŸŽฌ Watch the classic talk here: bit.ly/4aotulm

    #ComputerScience #SoftwareEngineering #SoftwareArchitecture #TechHistory

  53. Imagine inventing something, watching it become an industry standard, and then spending decades apologizing for it.

    That's exactly what computing pioneer Sir Tony Hoare did in his legendary talk, "Null References: The Billion-Dollar Mistakeโ€.

    As part of the #InfoQ20 campaign, we're revisiting this classic archive video - a must-watch for every software practitioner. It's a powerful lesson in how a seemingly small design decision can shape the entire software industry for decades.

    ๐ŸŽฌ Watch the classic talk here: bit.ly/4aotulm

    #ComputerScience #SoftwareEngineering #SoftwareArchitecture #TechHistory

  54. Imagine inventing something, watching it become an industry standard, and then spending decades apologizing for it.

    That's exactly what computing pioneer Sir Tony Hoare did in his legendary talk, "Null References: The Billion-Dollar Mistakeโ€.

    As part of the #InfoQ20 campaign, we're revisiting this classic archive video - a must-watch for every software practitioner. It's a powerful lesson in how a seemingly small design decision can shape the entire software industry for decades.

    ๐ŸŽฌ Watch the classic talk here: bit.ly/4aotulm

    #ComputerScience #SoftwareEngineering #SoftwareArchitecture #TechHistory

  55. Imagine inventing something, watching it become an industry standard, and then spending decades apologizing for it.

    That's exactly what computing pioneer Sir Tony Hoare did in his legendary talk, "Null References: The Billion-Dollar Mistakeโ€.

    As part of the #InfoQ20 campaign, we're revisiting this classic archive video - a must-watch for every software practitioner. It's a powerful lesson in how a seemingly small design decision can shape the entire software industry for decades.

    ๐ŸŽฌ Watch the classic talk here: bit.ly/4aotulm

    #ComputerScience #SoftwareEngineering #SoftwareArchitecture #TechHistory