A developer’s guide to systematic prompting: mastering negative constraints, structured JSON output, and multi-hypothesis verbal sampling.
Most developers treat prompting as an afterthought – write something appropriate, observe the output and iterate if necessary. This approach works until reliability becomes critical. As LLMs advance into production …
Musk vs. Altman Week 1: Elon Musk says he’s been duped, warns AI could kill us all, and admits XAI distills OpenAI’s model
“I was a fool to provide them with free funding to launch a startup,” Musk told the jury. He said that when he co-founded OpenAI with Altman and Brockman in …
AI is flooding music streaming services – but who wants it?
it is stepbackA weekly newsletter that presents an essential story from the tech world. Follow Terrence O’Brien for more on how AI is changing music and the music industry. stepback …
A common charging habit was silently draining my iPhone’s battery – here’s the solution
Adrian Kingsley-Hughes/ZDNET Follow ZDNET: Add us as a favorite source On Google. ZDNET Highlights Extreme temperatures are a major factor in premature battery failure. Car charging is a perfect storm …
Under a cloud: growing resentment against giant datacentres springing up in Australian cities computing
wWest Footscray resident Sean Brown takes his 19-month-old boy to the park, their walk passing a grand new building billed as “Australia’s largest hyperscale AI factory”, a datacenter called M3. …
OpenWeight Text-to-Speech with Voxtral TTS
Image by editor # Introduction Voice-enabled applications are everywhere from virtual assistants to customer service chatbots. But for developers, creating natural-sounding speech in apps often means relying on expensive cloud …
Innodata GenAI Summit London: May 21, 2026
Who is ready for a different kind of AI event? Most AI conferences follow the same formula: Keynotes that rehash last month’s announcements, panels that refresh familiar debates, and networking …
Build a multi-agent AI workflow for biological network modeling, protein interactions, metabolism, and cell signaling simulations
class CellSignalingSimulationAgent: def run(self, df_signal: pd.DataFrame) -> AgentResult: peak_receptor = float(df_signal(“receptor_active”).max()) peak_kinase = float(df_signal(“kinase_active”).max()) peak_tf = float(df_signal(“tf_active”).max()) t_receptor = float(df_signal.loc(df_signal(“receptor_active”).idxmax(), “time”)) t_kinase = float(df_signal.loc(df_signal(“kinase_active”).idxmax(), “time”)) t_tf = float(df_signal.loc(df_signal(“tf_active”).idxmax(), “time”)) final_state …