AI Tools & Trends 2024–2025

What changed, what matters, what to use

Alex Efremov

Agenda

  • 2024-2025 AI Milestones
  • Reasoning LLMs & Test-Time Compute
  • Voice Stack
  • Agentic AI
  • Evals & Monitoring
  • AI Development
⏰
Timeline
🧠
Reasoning
🎤
Voice
🤖
Agents
📊
Evals
⌨️
Coding

Key Milestones

Timeline

2024: The Year AI Went Mainstream

Major Releases

  • OpenAI o1 - Reasoning models
  • Sora - Text-to-video
  • ChatGPT 4o - Multimodal
  • Claude 3.5 - Enhanced reasoning
  • Gemini 2.0 - 2x faster
  • Apple Intelligence - Platform integration
  • Llama 3.2 - Open multimodal

Key Innovations

  • Voice Mode - Real-time AI
  • ChatGPT Search - AI web search
  • Multimodal AI - All modalities
  • Agentic AI - Autonomous
  • EU AI Act - Global standards
  • GenCast - AI forecasting
  • NVIDIA - Chip dominance

2025: Era of Reasoning & Agents

  • Vibe Coding - Andrej Karpathy (Jan)
  • Sesame - Open source SOTA TTS (Feb)
  • Gemini 2.5 Pro - 2M tokens (Mar)
  • Qwen3 - Robotics AI (Apr)
  • Claude Sonnet and Opus 4 - Top benchmarks (May)
  • Grok 4 Heavy - $300/mo tier
  • Kimi K2 - Trillion params open source
  • Qwen3-Coder - Agentic coding new SOTA

Reasoning at scale • Agentic workflows • Multimodal default • Open vs closed race

Regular vs Reasoning Models

Reasoning models think in multiple steps and spend extra compute at inference (TTC) to get higher accuracy.

  • Single-pass → Multi-step deliberation
  • Tool calls during thinking
  • Test-Time Compute (TTC) boosts correctness
  • Benchmarks jump (GPQA, MMLU-Pro, AIME, HLE)

Traditional

Prompt → Answer

→

Reasoning

Prompt → Plan → Tool → Reflect → Answer

o3/o4-mini system card 🔗 DeepSeek R1 GitHub 🔗 Anthropic Opus 4 PDF 🔗

Test-Time Compute (TTC) in Practice

More tokens spent reasoning = better results, with diminishing returns.

  • Allocate 'thinking tokens'
  • Higher TTC ≈ +X% on GPQA/MATH
  • APIs expose 'budget' or 'steps' knobs
TTC Budget (tokens) Accuracy % Low Medium High Max
System cards with TTC curves 🔗
TTC Curves
TTC Curves

DeepSeek R1

R1 proves open-weight reasoning can rival closed models.

Metric Value
Parameters 685B (MoE)
Training Tokens 14.8T
GPQA 81.0
License MIT
  • Strengths: reasoning, math, multi-step logic
  • Caveat: needs safety filters
HF repo 🔗 R1 paper/blog 🔗

Claude Opus 4 / Sonnet 4

Anthropic's hybrid reasoning + tool use models handle day-long tasks & coding reliably.

  • Opus 4: highest intelligence tier
  • Sonnet 4: cheaper, strong reasoning
  • Can work continuously for hours on complex tasks
Sonnet 4 Opus 4 o3 82.1 85.3 87.7 GPQA Performance
Anthropic PDF/system card 🔗
Claude Benchmarks

Kimi K2

Moonshot Kimi K2 delivers open, massive MoE reasoning at scale (1T params, 384 experts).

  • 1T params / 15.5T tokens
  • Strong math & logic (AIME 77.5)
  • Apache-style open license
MMLU GPQA AIME Code Math Logic K2
Moonshot GitHub 🔗 OpenRouter entry 🔗
Kimi K2 Benchmarks

Qwen3 Coder (480B MoE)

Alibaba's specialist coding model crushing benchmarks with 480B params, 35B active.

Metric Value
Parameters 480B (35B active)
Context Length 256K (1M ext.)
SWE-Bench Verified SOTA open-source
CodeForces ELO Leading
  • Multi-language: Python, JS, Java, C++, Go, Rust+
  • Debugging & refactoring: automated optimization
  • Security: vulnerability detection
SWE-Bench CodeForces LiveCodeBench BFCL
Qwen GitHub 🔗 HuggingFace 🔗 Technical Report 🔗
Qwen3 Coder Benchmark

Leaderboard (Jul 2025)

Snapshot of key reasoning contenders & a couple headline metrics.

Model Params Tokens MMLU GPQA
Grok 4 5000B 80T - 88.9
Claude Opus 4 1200B 100T - 83.3
DeepSeek R1 685B 14.8T 93.4 81.0
Qwen3 Coder 480B - - -
Kimi K2 1000B 15.5T 89.5 75.1
Qwen3-235B 235B 36T 93.1 77.5

Voice Pipeline Overview

Reliable voice agents need a tuned pipeline: STT → Reasoner → TTS or V2V, optimized for latency & quality.

  • STT: fast/accurate
  • LLM: reason/tool-use
  • TTS/V2V: expressive & low-latency
  • Barge-in, interruptions handling
🎤

STT

Audio → Text

→
🧠

LLM

Reason & Tools

→
🔊

TTS/V2V

Text → Audio

Speech-to-Text: Accuracy vs Latency

Choose STT by WER, latency, language coverage & diarization.

Word Error Rate (%) Whisper v3 GPT-4o STT Deepgram AssemblyAI 2.3% 2.8% 2.1% 2.5%
  • WER ~2-3%, Latency ~50-200ms
  • Streaming + partial transcripts
  • Multi-language & diarization
~150ms
Typical Latency
Whisper API 🔗 Deepgram docs 🔗 AssemblyAI 🔗

TTS: Naturalness, Control & Styles

Modern TTS offers controllable emotion/style with near-human naturalness.

Voice Waveform + Pitch Waveform Pitch
  • ElevenLabs v3: style, emotion, low latency
  • PlayHT, Hume Octave, Papla P1 (streaming APIs)
  • Sesame
ElevenLabs API 🔗 PlayHT docs 🔗 Hume Octave 🔗
ElevenLabs v3 Demo

Voice-to-Voice (V2V) Models

V2V is here: direct voice in → voice out, enabling fluid, natural dialogs.

  • OpenAI Realtime,
  • Gemini Live
  • Sesame & others emerging
Voice In Voice Encoder Voice Embeddings Voice Decoder Voice Out ~200-500ms end-to-end
OpenAI Realtime API 🔗 Gemini Live docs 🔗

Glueing It Together: Agents, Tools & Protocols

MCP and graph frameworks standardize tool access and agent flow.

  • MCP: tool servers for LLMs
  • Graph orchestration (LangGraph, Haystack 2, CrewAI)
  • Realtime frameworks for voice agents
MCP Database APIs Files Web Email Cloud
Anthropic MCP docs 🔗

Model Context Protocol (MCP) Deep Dive

MCP is rapidly becoming the default way to expose SaaS tools to models.

  • Spec defines: tools, prompts, schemas
  • Growing vendor support (CRMs, DBs, SaaS)
  • Easy local MCP servers
{ "jsonrpc": "2.0", "method": "tools/call", "params": { "name": "database_query", "arguments": { "query": "SELECT * FROM users", "database": "production" } } }
Official MCP docs 🔗
MCP Deep Dive

Graph-Oriented Orchestration

LangGraph & similar frameworks let you design agent flows as state graphs.

  • Nodes = steps/tools
  • Edges = transitions/conditions
  • Great for long-running workflows
Voice Call Intent Detect DB Lookup Response question? lookup?
LangGraph docs 🔗 CrewAI GitHub 🔗

Realtime Agent Frameworks (Voice-Focused)

Frameworks like Pipecat, Vapi, Retell simplify low-latency streaming & barge-in.

  • Compare: streaming, cost, integrations
  • Pick based on latency & features
Framework Low-Latency? STT/TTS Built-in? Pricing
Vapi ✅ Yes ✅ Built-in $0.05/min
Retell ✅ Yes ✅ Managed $0.08/min
Bland ⚠️ Medium ✅ Full Stack $0.12/min
Pipecat ✅ Yes ✅ Integrated Open Source
Pipecat 🔗 Vapi 🔗 Retell 🔗

Measure to Improve

Without evals and telemetry, you can't iterate intelligently.

  • Offline evals (benchmarks, unit tests)
  • Online metrics (latency, success, UX)
  • Feedback loops & guardrails
98.2%
Success Rate
1.2s
Avg Latency
4.3/5
User Rating
Performance Over Time
📊 Dashboard Mock

Modern LLM Evaluation Landscape

Comprehensive evaluation tools for prompt testing, model comparison, and security assessment.

📊 Performance Testing

  • promptfoo - Red teaming & regression tests
  • OpenAI Evals - Official benchmark framework

🎯 Advanced Evaluation

  • DeepEval - RAG & conversational metrics
  • PromptWizard - Self-evolving optimization

Why Evaluation Matters

"Creating high quality evals is one of the most impactful things you can do" - Greg Brockman, OpenAI

promptfoo: AI Red Teaming & Testing

Local LLM evaluation with security focus - 7.9k GitHub stars

🔍 Key Features

  • Red teaming & vulnerability scanning
  • Side-by-side model comparisons
  • 100% local execution
  • CI/CD integration ready

Quick Start

npx promptfoo@latest init
npx promptfoo eval

🎯 Use Cases

  • Prompt engineering optimization
  • Model performance comparison
  • AI application security testing
  • Automated vulnerability reporting

Supported Providers

OpenAI, Anthropic, Azure, Bedrock, Ollama

promptfoo GitHub 🔗 Documentation 🔗

OpenAI Evals: Official Benchmark Framework

Open-source evaluation registry - 16.7k GitHub stars

🏗️ Framework Features

  • Open registry of benchmarks
  • Custom evaluations for specific use cases
  • Private evals with proprietary data
  • Model-graded evaluations

Installation

pip install evals

📊 Evaluation Types

  • Basic model evaluations
  • Prompt chain assessments
  • Tool-using agent tests
  • Custom workflow patterns
"Creating high quality evals is one of the most impactful things you can do" - Greg Brockman
OpenAI Evals GitHub 🔗 Documentation 🔗

DeepEval: Comprehensive LLM Evaluation

Local evaluation framework with extensive metrics - 9.8k GitHub stars

🎯 Core Metrics

  • RAG metrics: Answer Relevancy, Faithfulness
  • Agentic metrics: Task Completion
  • Safety metrics: Hallucination, Bias, Toxicity
  • Conversational: Knowledge Retention

Quick Setup

pip install -U deepeval

🔧 Advanced Features

  • Pytest integration
  • Custom metric creation
  • Synthetic dataset generation
  • Red team testing (40+ vulnerabilities)
  • MMLU & HellaSwag benchmarking

Integrations

LlamaIndex, Hugging Face, Cloud Platform

DeepEval GitHub 🔗 Documentation 🔗

PromptWizard: Self-Evolving Optimization

Microsoft's AI-driven prompt optimization framework

🧙‍♂️ Self-Evolution

  • AI generates its own prompts
  • Self-critique and refinement
  • Synthetic examples generation
  • Chain of Thought optimization

Installation

git clone microsoft/PromptWizard
pip install -e .

📈 Usage Scenarios

  • Optimize prompts without examples
  • Generate synthetic training data
  • Optimize with existing training data
  • Task-aware prompt refinement

Tested Datasets

GSM8k, SVAMP, AQUARAT, Instruction Induction

PromptWizard GitHub 🔗 Research Paper 🔗

LLM Evaluation Best Practices

Strategic approach to comprehensive AI model assessment

🏗️ Foundation Layer

  • Automated Testing: OpenAI Evals for standardized benchmarks
  • Security Scanning: promptfoo for vulnerability assessment
  • CI/CD Integration: Run evals on every deployment

🎯 Advanced Layer

  • RAG Evaluation: DeepEval for retrieval quality
  • Prompt Optimization: PromptWizard for self-improvement
  • Custom Metrics: Domain-specific evaluations

Evaluation Strategy Framework

📊

Baseline
Standard benchmarks

→
🔍

Security
Red team testing

→
🎯

Domain
Custom metrics

→
🔄

Optimize
Continuous improvement

Voice Agent KPIs

Track voice-specific metrics: barge-in success, first-token latency, intent accuracy.

  • Latency (STT, LLM, TTS)
  • Intent success / fallback rate
  • CSAT proxies (sentiment, repeat calls)
STT WER: 2.1% Latency: 150ms LLM Intent: 95% Latency: 800ms TTS Quality: 4.2/5 Latency: 300ms UX CSAT: 4.1/5 Barge-in: 87%

Guardrails & Safety Checks

Insert safety filters pre & post model to prevent PII leaks and jailbreaks.

  • Input filter → LLM → Output filter
  • PII scrub, toxicity detection
  • Red-team tests
User Input INPUT FILTER PII, Toxic LLM OUTPUT FILTER Safety, Quality Response
Safety toolkits 🔗 Red-team guidelines 🔗

From Autocomplete to Swarm Coders

Coding tools now plan, write, test, and review code—not just autocomplete.

  • IDE copilots → Repo-wide agents
  • Autonomy: run tests, open PRs
  • Speed + quality gains
Autocomplete Pair Programming Swarm Coding Code completion Chat, explain, debug Plan, test, deploy

Top IDE/Agent Tools - Part 1

Primary AI-powered development environments.

Cursor

Best for: VS Code replacement with AI

Claude Code

Best for: Complex reasoning tasks

Replit Agent

Best for: Full-stack prototyping

Caveat: Cloud-only environment

GitHub Copilot

Best for: Code completion

Caveat: Limited reasoning

Top IDE/Agent Tools - Part 2

Additional specialized AI development tools.

Lovable

Best for: Frontend development

Caveat: Limited backend features

🤖 Cline

Best for: VS Code extension agent

Caveat: Early development stage

⚫ Void Editor

Best for: Minimal AI-first IDE

Caveat: Limited plugin ecosystem

Bolt.new

Best for: Instant web apps

Caveat: Limited customization

Prompt & Command Layers (TaskMaster, Superprompt)

Reusable prompt macros and task planners supercharge IDE agents.

// TaskMaster Template
@template feature-implementation
@context ${codebase}
@requirements ${specs}
@output structured-plan
1. Analyze requirements
2. Design architecture
3. Implement & test
4. Document changes
📝 Template Example
  • TaskMaster: task planning for agents
  • Reusable 'workbench' prompts
  • Command palettes inside IDE

Productivity Boost

Standardized workflows reduce context switching and improve consistency

TaskMaster site 🔗 Prompt libraries 🔗

Repo-Wide Agents: Branching, Testing, PRs

Agents can now autonomously modify repos, run CI, open PRs, self-review.

  • Branch per task
  • Auto tests & lint
  • Agent reviews agent
Agent 1 CI/CD Agent 2 Create Branch Write Code Open PR Run Tests Lint & Build Code Review 🌿 ⚙️ 👁️
Swarm GitHub 🔗 AutoGen docs 🔗 CrewAI GitHub 🔗

Interactive Demo: Create 3D Game

This game was created live during the presentation in Claude Code.

Game Demo Screenshot ▶ Play Game

Key Takeaways

Summarize the entire talk in five crisp points.

  • 🧠 Reasoning + TTC is the new baseline for hard problems.
  • 🎤 Voice-to-Voice & realtime stacks are production-ready.
  • 🔗 MCP becomes the new standard for orchestration.
  • 📊 Evals & metrics guard your quality.
  • ⌨️ Coding agents can 3–10× productivity if set up right.

Resources & Link Dump

All links in one place - key AI development tools and frameworks.

Thank You for Your Attention!

Alexander Efremov
AI Expert, Aspirity Company

✉️ ae@aspirity.com | Telegram: @sabbah13

Alexander Efremov
Download PDF