Ritesh S.

Helping Businesses with Branding, Packaging, UI/UX, WordPress, Shopify/WooCommerce, Web & Software Development | Founder & CEO

[Ritesh S. @riteshUX]

@sama Widespread poverty in the world

[JustAnotherPM | Sid @JustAnotherPM]

Product managers, if you think the AI model is the hard part of building AI products, read this.

Researchers from UCL reverse-engineered Claude Code's source. 512,000 lines of code.

Only 1.6% is AI decision logic.

The other 98.4% is operational infrastructure.
- Permission gates.
- Tool routing.
- Context management.
- Error recovery.
- Safety systems.

Here is why this matters:

1. 𝗧𝗵𝗲 𝗺𝗼𝗱𝗲𝗹 𝗶𝘀 𝗼𝗻𝗲 ��𝘂𝗻𝗰𝘁𝗶𝗼𝗻 𝗰𝗮𝗹𝗹. The harness around it is 200,000 lines. As models converge in capability, the harness becomes the moat.

2. 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗔𝗜 𝗶𝘀 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴. If you are only thinking about prompts, you are working on 1.6% of the problem.

3. 𝗧𝗵𝗲 𝗹𝗮𝘆𝗲𝗿𝘀 𝘁𝗵𝗮𝘁 𝗺𝗮𝘁𝘁𝗲𝗿: CLAUDE[dot]md for context. Hooks for guardrails. MCP for external tools. Subagents for parallel work. Skills for reusable patterns.

Paper: https://t.co/VeiU5q0n4S

I've built a course using Claude Code that teachees you to to build all 5 layers in Claude Code taught by Claude Code

It's FREE

[LogoArchive @LogoArchive]

Discover over 500 historical logos in colour 👉 https://t.co/VGmPvDgMdU

#logos #branding #symbols

[Andrew Ng @AndrewYNg]

There will be no AI jobpocalypse.

The story that AI will lead to massive unemployment is stoking unnecessary fear. AI — like any other technology — does affect jobs, but telling overblown stories of large-scale unemployment is irresponsible and damaging. Let’s put a stop to it.

I’ve expressed skepticism about the jobpocalypse in previous posts. I’m glad to see that the popular press is now pushing back on this narrative. The image below features some recent headlines.

Software engineering is the sector most affected by AI tools, as coding agents race ahead. Yet hiring of software engineers remains strong! So while there are examples of AI taking away jobs, the trends strongly suggest the net job creation is vastly greater than the job destruction — just like earlier waves of technology. Further, despite all the exciting progress in AI, the U.S. unemployment rate remains a healthy 4.3%.

Why is the AI jobpocalypse narrative so popular? For one thing, frontier AI labs have a strong incentive to tell stories that make AI technology sound more powerful. At their most extreme, they promote science-fiction scenarios of AI “taking over” and causing human extinction. If a technology can replace many employees, surely that technology must be very valuable!

Also, a lot of SaaS software companies charge around $100-$1000 per user/year. But if an AI company can replace an employee who makes $100,000 — or make them 50% more productive — then charging even $10,000 starts to look reasonable. By anchoring not to typical SaaS prices but to salaries of employees, AI companies can charge a lot more.

Additionally, businesses have a strong incentive to talk about layoffs as if they were caused by AI. After all, talking about how they’re using AI to be far more productive with fewer staff makes them look smart. This is a better message than admitting they overhired during the pandemic when capital was abundant due to low interest rates and a massive government financial stimulus.

To be clear, I recognize that AI is causing a lot of people’s work to change. This is hard. This is stressful. (And to some, it can be fun.) I empathize with everyone affected. At the same time, this is very different from predicting a collapse of the job market.

Societies are capable of telling themselves stories for years that have little basis in reality and lead to poor society-wide decision making. For example, fears over nuclear plant safety led to under-investment in nuclear power. Fears of the “population bomb” in the 1960s led countries to implement harsh policies to reduce their populations. And worries about dietary fat led governments to promote unhealthy high-sugar diets for decades.

Now that mainstream media is openly skeptical about the jobpocalypse, I hope these stories will start to lose their teeth (much like fears of AI-driven human extinction have).

Contrary to the predictions of an AI jobpocalypse, I predict the opposite: There will be an AI jobapalooza! AI will lead to a lot more good AI engineering jobs, and I’m also optimistic about the future of the overall job market. What AI engineers do will be different from traditional software engineering, and many of these jobs will be in businesses other than traditional large employers of developers. In non-AI roles, too, the skills needed will change because of AI. That makes this a good time to encourage more people to become proficient in AI, and make sure they’re ready for the different but plentiful jobs of the future!

[Original text in The Batch newsletter.]

[Desi Corey @Ramjango_1762]

This duo highlighted in a humorous way those issues of the common people which are still important for the common people today.
No comedian can match this duo.

[Ritesh S. @riteshUX]

@TheNewspinch Damage to key infrastructure should be under the purview of disaster management.

[Andrew Ng @AndrewYNg]

New course: Build agents that respond to users with not only plaintext, but custom UIs like charts, forms, and whiteboards, generated on demand and displayed right in the chat. This short course is built in partnership with @CopilotKit and taught by @ataiiam, co-founder of CopilotKit.

You'll learn three approaches: Your agent can pick from custom components you build, like charts and forms. It can compose new layouts from a set of building blocks you provide, like rows, cards, and text. Or it can incorporate existing third-party apps, like a whiteboard or a calendar, right inside the conversation.

Skills you’ll gain:
- Build agents that render custom components like charts and forms on demand
- Build an app where the agent and user collaborate on shared data, beyond just the chat window
- Place third-party apps like maps, calendars, and whiteboards right in your interface

Join and build agents that give users something to see and act on! https://t.co/lvMy0YdF3z

[Punit Pania @Punit_Pania]

Please help me spread the word and contribute: https://t.co/gGumOi4KWo
And please look up @internetfreedom.in if you haven't already 🖖
Best of Luck @apar1984

[Andrew Ng @AndrewYNg]

Coding agents are accelerating different types of software work to different degrees. When we architect teams, understanding these distinctions helps us to have realistic expectations. Listing functions from most accelerated to least, my order is: frontend development, backend, infrastructure, and research.

Frontend development — say, building a web page to serve descriptions of products for an ecommerce site — is dramatically sped up because coding agents are fluent in popular frontend languages like TypeScript and JavaScript and frameworks like React and Angular. Additionally, by examining what they have built by operating a web browser, coding agents are now very good at closing the loop and iterating on their own implementations. Granted, LLMs today are still weak at visual design, but given a design (or if a polished design isn’t important), the implementation is fast!

Backend development — say, building APIs to respond to queries requesting product data — is harder. It takes more work by human developers to steer modern models to think through corner cases that might lead to subtle bugs or security flaws. Further, a backend bug can lead to non-intuitive downstream effects like a corrupted database that occasionally returns incorrect results, which can be harder to debug than a typical frontend bug. Finally, although database migrations can be easier with coding agents, they’re still hard and need to be handled carefully to prevent data loss. While backend development is much faster with coding agents, they accelerate it less, and skilled developers still design and implement far better backends than inexperienced ones who use coding agents.

Infrastructure. Agents are even less effective in tasks like scaling an ecommerce site to 10K active uses while maintaining 99.99% reliability. LLMs' knowledge is still relatively limited with respect to infrastructure and the complex tradeoffs good engineers must make, so I rarely trust them for critical infra decisions. Building good infrastructure often requires a period of testing and experimentation, and coding agents can help with that, but ultimately that’s a significant bottleneck where fast AI coding does not help much. Lastly, finding infrastructure bugs — say, a subtle network misconfiguration — can be incredibly difficult and requires deep engineering expertise. Thus, I’ve found that coding agents accelerate critical infrastructure even less than backend development.

Research. Coding agents accelerate research work even less. Research involves thinking through new ideas, formulating hypotheses, running experiments, interpreting them to potentially modify the hypotheses, and iterating until we reach conclusions. Coding agents can speed up the pace at which we can write research code. (I also use coding agents to help me orchestrate and keep track of experiments, which makes it easier for a single researcher to manage more experiments.) But there is a lot of work in research other than coding, and today’s agents help with research only marginally.

Categorizing software work into frontend, backend, infra, and research is an extreme simplification, but having a simple mental model for how much different tasks have sped up has been useful for how I organize software teams. For example, I now ask front-end teams to implement products dramatically faster than a year ago, but my expectations for research teams have not shifted nearly as much.

I am fascinated by how to organize software teams to use coding agents to achieve speed, and will keep sharing my findings in future posts.

[Original text: https://t.co/rnnVWqebVe ]

[Ritesh S. @riteshUX]

@amitkilhor In what situation they can actually be useful? Apart from missiles and drones attacks. I don't believe Earthquakes will give this much time to send alerts. What's coming?

[Ritesh S. @riteshUX]

@vijayshekhar That said the USA controls 71% of the world's cumulative AI compute!

[Ritesh S. @riteshUX]

@acorn @TVMohandasPai It's NOT about the brand consultants - it's about who gave this option a green. Who approved this? That is someone from Godrej itself. The agency presents multiple concepts - the client had to take the final call.

[Ritesh S. @riteshUX]

@manas_muduli No bills mean no business either.

[Ritesh S. @riteshUX]

@abhinaymaths जितना बड़ा पद - उतना बड़ा अहम। Unless the parents are respected devoid of their socio-economic situation and treated as equals by the education fraternity, nothing is going to improve. The whole outcome of schools remains as many toppers! Character building is THE goal.

[Ritesh S. @riteshUX]

@alvinfoo SaaS -> AaaS

[Ritesh S. @riteshUX]

@Nithin0dha What exactly did the macro thing happen around 1982 ? Since then it's been up and up. Ever rising.

[Ritesh S. @riteshUX]

@cometwtf Nero

[кєνín @notcee_fan]

Twitter brand is so strong it was renamed and it’s still Twitter.

[Ritesh S. @riteshUX]

@Brs_Teja This is applicable to all party in power to diminish this VIP culture. Respect for everybody's work and time.

[Ritesh S. @riteshUX]

@sardesairajdeep @TVMohandasPai I believe the problem is not protest itself but the blockade. One lane should always be open for traffic 🚦