An alarming 94.6% of AI leaders in companies acknowledge the pressing need for AI capabilities amid intensified competition, revealing stagnation in many projects.

Flowfinity provides crucial insights for business leaders to effectively prioritize AI projects, ensuring a measurable return on investment.

The IT freelance market is evolving, with AI projects seeing substantial growth. Explore trends and the implications for freelance workers and companies.

SHINE has been chosen for two key AI projects under the DOE's Genesis Mission, advancing nuclear fuel recycling. The projects aim to improve recycling processes.

from transformers import AutoModelForCausalLM, AutoTokenizer from autogen_agentchat.agents import AssistantAgent import torch model_id = "/home/mony/ai_projects/ai-agent/mistralaiMistral-7B-Instruct-v0.3" model_client = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto") assistant = AssistantAgent(name="assistent", model_client=model_client) result = await assistant.run(task = "what's the captial of india?") --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) Cell In[9], line 1 ----> 1 result = await assistant.run(task = "what's the captial of india?") File ~/ai_projects/ai-agent/venv/lib/python3.12/site-packages/autogen_agentchat/agents/_base_chat_agent.py:149, in BaseChatAgent.run(self, task, cancellation_token, output_task_messages) 147 else: 148 raise ValueError(f"Invalid message type in sequence: {type(msg)}") --> 149 response = await self.on_messages(input_messages, cancellation_token) 150 if response.inner_messages is not None: 151 output_messages += response.inner_messages File ~/ai_projects/ai-agent/venv/lib/python3.12/site-packages/autogen_agentchat/agents/_assistant_agent.py:896, in AssistantAgent.on_messages(self, messages, cancellation_token) 882 async def on_messages( 883 self, 884 messages: Sequence[BaseChatMessage], 885 cancellation_token: CancellationToken, 886 ) -> Response: 887 """Process incoming messages and generate a response. 888 889 Args: (...) 894 Response containing the agent's reply 895 """ --> 896 async for message in self.on_messages_stream(messages, cancellation_token): 897 if isinstance(message, Response): 898 return message File ~/ai_projects/ai-agent/venv/lib/python3.12/site-packages/autogen_agentchat/agents/_assistant_agent.py:953, in AssistantAgent.on_messages_stream(self, messages, cancellation_token) 951 # STEP 4: Run the first inference 952 model_result = None --> 953 async for inference_output in self._call_llm( 954 model_client=model_client, 955 model_client_stream=model_client_stream, 956 system_messages=system_messages, 957 model_context=model_context, 958 workbench=workbench, 959 handoff_tools=handoff_tools, 960 agent_name=agent_name, 961 cancellation_token=cancellation_token, 962 output_content_type=output_content_type, 963 message_id=message_id, 964 ): 965 if isinstance(inference_output, CreateResult): 966 model_result = inference_output File ~/ai_projects/ai-agent/venv/lib/python3.12/site-packages/autogen_agentchat/agents/_assistant_agent.py:1084, in AssistantAgent._call_llm(cls, model_client, model_client_stream, system_messages, model_context, workbench, handoff_tools, agent_name, cancellation_token, output_content_type, message_id) 1067 """Call the language model with given context and configuration. 1068 1069 Args: (...) 1081 Generator yielding model results or streaming chunks 1082 """ 1083 all_messages = await model_context.get_messages() -> 1084 llm_messages = cls._get_compatible_context(model_client=model_client, messages=system_messages + all_messages) 1086 tools = [tool for wb in workbench for tool in await wb.list_tools()] + handoff_tools 1088 if model_client_stream: File ~/ai_projects/ai-agent/venv/lib/python3.12/site-packages/autogen_agentchat/agents/_assistant_agent.py:1640, in AssistantAgent._get_compatible_context(model_client, messages) 1637 u/staticmethod 1638 def _get_compatible_context(model_client: ChatCompletionClient, messages: List[LLMMessage]) -> Sequence[LLMMessage]: 1639 """Ensure that the messages are compatible with the underlying client, by removing images if needed.""" -> 1640 if model_client.model_info["vision"]: 1641 return messages 1642 else: File ~/ai_projects/ai-agent/venv/lib/python3.12/site-packages/torch/nn/modules/module.py:1940, in Module.__getattr__(self, name) 1938 if name in modules: 1939 return modules[name] -> 1940 raise AttributeError( 1941 f"'{type(self).__name__}' object has no attribute '{name}'" 1942 ) AttributeError: 'MistralForCausalLM' object has no attribute 'model_info'

MobiDev has introduced its AI Implementation Failure Test, aimed at helping business leaders identify issues in stalled AI projects and guiding recovery.

Everpure introduces Evergreen//One for FlashBlade//EXA and Everpure Data Stream Beta, aimed at simplifying enterprise AI projects by eliminating complexity and reducing costs.

Amana reports impressive Q3 2025 results, with major growth in profits. The company focuses on optimizing resources and innovative strategies for sustainable success.

Explore six exciting projects from the GARAGE Program's 94th term at 100BANCH, each aiming to solve societal challenges and foster innovation.