The Role of Prompting in Agentic AI with Python and OpenAI - What is an AI Agent?
So, what exactly is an AI Agent? At its core, an agent is an intelligent system capable of perceiving its environment, making decisions, and taking actions to achieve specific goals. Key characteristics include autonomy, adaptability, and interaction with its surroundings.
Cd14526-Agentic AI-C1-A01b-Intro-V1 - YouTube
C1l1 Introductiondemo V2 - YouTube
Cd14526-Agentic AI-C1-A02-Role-Based Prompting-V1 - YouTube
a "solution" for hosing crisis?
made from metal, various layouts/prices?
ships from China...
Amazon Is Selling a Solar-powered Tiny House Under $15K
Microsoft Wants to Build Self-Sufficiency: $1M AI Agents, Proprietary Chips, and The AGI Race #216 - YouTube @ Moonshots with Peter Diamandis
The idea that APIs will be replaced by AI agents is explained by Mustafa Suleyman as part of a fundamental transition in how we interact with computing (3:32).
Here's a breakdown of this concept:
The Future of AI: From User Interfaces to Agents and Companions (0:23-4:47) Mustafa Suleyman, CEO of Microsoft AI, explains that the fundamental transition in AI is from a world of operating systems, search engines, apps, and browsers to a world of "agents and companions." These AI models will function as personalized assistants, capable of handling tasks and understanding context, leading to less direct human computing.
The "AGI Race" is a Misconception (0:03-0:05, 31:54-32:29) Suleyman argues against the notion of a "race" to achieve AGI (Artificial General Intelligence), stating that it implies a zero-sum game with a finish line, which doesn't align with how technology and knowledge proliferate.
The Modern Turing Test: Economic Benchmarks for AI Autonomy (10:27-12:22) Suleyman reiterates his proposal for a "modern Turing test," focusing on economic benchmarks for AI agents rather than theoretical ones. This involves measuring an AI's ability to generate economic value.
The Inflection Point: Rapid Progress and Underreaction (8:50-9:05, 16:04-17:10) The discussion highlights that AI has reached an "inflection point" where models are in production and fundamentally changing human interactions. Despite this rapid progress, there is an underreaction from people who underestimate the pace of change.
AI's Impact on Science and Engineering (22:28-24:50) The conversation touches on the surprising ability of AI to learn logical reasoning and apply it across different domains, particularly in scientific and engineering challenges.
The Unexpected Accessibility and Cost Reduction of AI (26:16-27:54) Suleyman expresses surprise at how cheap and accessible AI has become, noting a significant reduction in inference costs.
AI Alignment, Containment, and the Illusion of Consciousness (36:06-39:20) Suleyman emphasizes the importance of safety, alignment, and containment of AI. He also discusses the perception of conscious AI as an illusion, distinguishing it from sentience and highlighting the potential problems of anthropomorphizing AI.
Russell is the co-author with Peter Norvig of the authoritative textbook of the field of AI: Artificial Intelligence: A Modern Approach used in more than 1,500 universities in 135 countries.
An AI Expert Warning: 6 People Are (Quietly) Deciding Humanity’s Future! We Must Act Now! - YouTube
AI Expert STUART RUSSELL, exposes the trillion-dollar AI race, why governments won’t regulate, how AGI could replace humans by 2030, and why only a nuclear-level AI catastrophe will wake us up Professor Stuart Russell O.B.E. is a world-renowned AI expert and Computer Science Professor at UC Berkeley. He holds the Smith-Zadeh Chair in Engineering and directs the Center for Human-Compatible AI, and is also the bestselling author of the book “Human Compatible: AI and the Problem of Control".Based on the interview with Professor Stuart Russell, here are the key ideas and messages extracted from the transcript.
Professor Russell’s message is one of urgent skepticism. He is "appalled" by the current trajectory and believes that we are building "imitation humans" designed to replace us rather than "tools" designed to help us. He suggests that if he had a button to stop all AI progress forever, he might actually press it, as the current "P of doom" (probability of extinction) is unacceptably high.
Will this file format REPLACE JSON!? - YouTube
TOON (Token-Oriented Object Notation) is a newer, leaner data format designed to be more efficient for Large Language Models (LLMs) than JSON, significantly cutting costs and improving speed by stripping redundant syntax (like quotes, braces, commas) and using indentation/tabular layouts, making it great for structured, uniform data; JSON remains the standard for general APIs but is verbose for AI, while TOON excels in token reduction and clarity for LLM tasks like agent inputs, though JSON is better for complex nesting.Here is how specific complex types are encoded:
TOON is most famous for compressing arrays of objects. Instead of repeating keys like JSON, it creates a header row.
key[count]{header1, header2}:users[3]{id, name, role}:
1, Alice, admin
2, Bob, editor
3, Charlie, viewer
tags[3]: "news", "tech", "2025"For nested data, TOON generally abandons the table structure and switches to an indentation-based syntax (similar to YAML or Python).
user:
id: 123
profile:
name: "Alice"
settings:
theme: "dark"
notifications: true
server:
config:
port: 8080
server.config.port: 8080
TOON tries to avoid quotation marks to save tokens, but it has specific rules for when they must be used.
status: active,), it must be wrapped in quotes.location: "Paris, France"\n and must be quoted.description: "First line.\nSecond line."If your data contains many commas (e.g., a list of addresses or sentences), using a comma as a separator becomes inefficient because you have to quote every string.
TOON allows you to change the delimiter to a Pipe | or Tab to avoid this. You declare the delimiter after the header.
| to avoid quoting commas):# Notice the '|' after the curly braces
articles[2]{id, title, summary}|:
101 | Hello, World! | A guide to "hello, world" apps.
102 | Data Formats | JSON, YAML, and TOON explained.
|, we didn't need to quote "Hello, World!" even though it has a comma.| Data Structure | JSON Approach | TOON Approach |
|---|---|---|
| List of Objects | [{"a":1}, {"a":2}] | list[2]{a}: 1, 2 (Tabular) |
| Nested Object | {"a": {"b": 1}} | a: b: 1 (Indented) |
| Deep Nesting | {"a": {"b": {"c": 1}}} | a.b.c: 1 (Dot Notation) |
| Text w/ Commas | "Hello, world" | "Hello, world" (Quoted) |
| Text w/ Pipes | "Hello, world" | Hello, world (Unquoted, using ` |
This is the critical "catch" with TOON. You have correctly identified the format's weakness: tabular data requires uniformity.
If you have an array where every object has different keys (sparse data, polymorphic objects, or "100 possible keys but only 2 used"), TOON abandons the table format.
Instead, it falls back to a standard indented list (YAML-style).
When the parser (or human) sees that the data is not uniform enough for a table, it switches to using dashes - for array items. This looks almost exactly like YAML.
Scenario: You have a list of "events," but each event type has totally different fields.
In JSON:
[
{"type": "click", "x": 10, "y": 20},
{"type": "keypress", "key": "Enter", "modifier": "Shift"},
{"type": "scroll", "delta": 500}
]
In TOON (Mixed/Sparse Mode):
events[3]:
- type: click
x: 10
y: 20
- type: keypress
key: Enter
modifier: Shift
- type: scroll
delta: 500
Note: We lost the table header compression, but we still saved tokens by removing all the curly braces {}, commas ,, and quotes "".
If you tried to force your "100 possible keys" scenario into a TOON table, it would actually be worse than JSON because you would need to print 98 empty delimiters for every row.
The Wrong Way (Forcing a Table): This is inefficient and unreadable.
# Don't do this!
items[2]{id, name, ...98 other keys...}:
1, itemA, , , , , , , ... , ,
2, itemB, , , , , , , ... , ,
The Right Way (TOON Standard): TOON handles this exactly like JSON or YAML would—by only writing the keys that exist.
items[2]:
- id: 1
name: itemA
# The other 98 null keys are simply omitted
- id: 2
name: itemB
custom_field: "special"
The strength of TOON is that it is context-aware. You can have one part of your file in "Table Mode" (for dense data) and another part in "List Mode" (for sparse/complex data) in the same document.
Example:
# A uniform list of users (Perfect for Table)
users[3]{id, role}:
1, admin
2, editor
3, viewer
# A complex, sparse audit log (Switch to List)
audit_log[2]:
- action: login
timestamp: 12:00
meta:
ip: 192.168.1.1
- action: error
code: 500
details: "Database timeout"
| Data Shape | JSON | TOON (Table) | TOON (List/Dash) | Verdict |
|---|---|---|---|---|
| Dense/Uniform | Heavy { "k":"v" } | Best v | Good k: v | TOON Table wins comfortably. |
| Sparse/Complex | Heavy { "k":"v" } | Invalid/Bad | Better k: v | TOON List wins (saves braces/quotes). |
So, for your specific case (complex objects, many nullable properties), TOON effectively behaves like minified YAML. You won't get the massive 50% reduction you see with tables, but you will still save roughly 15–20% in token count compared to JSON by removing the syntax noise.
While both TOON and JSON5 are "better versions of JSON" designed to be more human-readable, they have completely different goals.
Here is a breakdown of how they compare.
The most striking difference is in how they handle lists of objects.
JSON5 (The "Developer" Format)
{} and explicit keys for every item.{
// Comments are allowed!
users: [
{ id: 1, name: 'Alice', role: 'admin' },, // Trailing comma allowed
{ id: 2, name: 'Bob', role: 'editor' } // Keys repeated every time
]
}
TOON (The "AI" Format)
# Comments are also allowed (starts with #)
users[2]{id, name, role}:
1, Alice, admin
2, Bob, editor
| Feature | JSON5 | TOON |
|---|---|---|
| Primary Goal | Ease of editing for humans (Config files). | Token efficiency for LLMs (API costs). |
| Keys | Can be unquoted (if valid JS identifier). | Removed entirely in tabular arrays; unquoted in objects. |
| Arrays | Standard list [{}, {}]. | Tabular with headers (massive token savings). |
| Hierarchy | Braces { }. | Indentation (YAML-style). |
| Strings | Single ' or Double "; multi-line supported. | No quotes needed (unless containing delimiters). |
| Comments | // and /* */ (Supported). | # (Supported). |
| Token Cost | High. (Repeats keys, uses braces). | Low. (30–50% fewer tokens than JSON). |
name: 1,000 times.name once in the header. For large datasets, this makes TOON significantly smaller.Use JSON5 when:
.eslintrc, tsconfig).Use TOON when: