Podcasts about Reinforcement

Last time we spoke about the beginning of the Battle of Hong kong. Japan's rise as a threat to British interests accelerated through the 1920s and intensified after invading China. Britain's response remained contradictory—diplomatically conciliatory yet militarily apprehensive. While Singapore and Malaya received substantial reinforcements, Hong Kong was acknowledged indefensible, yet the 1936 Defence Scheme attempted to fortify it as an island fortress. Governor Northcote urgently advocated evacuation, recognizing the futility of defense. London rejected this, fearing it would demoralize China and forfeit sovereignty claims. Reinforcement requests from commanders like Grasett fell on deaf ears until Grasett independently lobbied Canadian leaders. This persistence finally convinced Churchill to dispatch two additional battalions. On December 8, 1941, Japanese forces attacked across three fronts with overwhelming force. Within hours, the RAF was destroyed on the ground, demolitions proved ineffective, and British units rapidly retreated toward the Gin Drinkers Line, facing an enemy that outnumbered and outmaneuvered them from the opening moments.   #217 The Battle of Hong Kong Part 2: the Island Aflame Welcome to the Fall and Rise of China Podcast, I am your dutiful host Craig Watson. But, before we start I want to also remind you this podcast is only made possible through the efforts of Kings and Generals over at Youtube. Perhaps you want to learn more about the history of Asia? Kings and Generals have an assortment of episodes on history of asia and much more  so go give them a look over on Youtube. So please subscribe to Kings and Generals over at Youtube and to continue helping us produce this content please check out www.patreon.com/kingsandgenerals. If you are still hungry for some more history related content, over on my channel, the Pacific War Channel where I cover the history of China and Japan from the 19th century until the end of the Pacific War. Around 1500 hours on 9 December, Colonel Doi of the 228th Regiment arrived at Needle Hill, just north of the Gin Drinkers Line, ahead of his main force. For two hours he studied the British defensive positions through binoculars, noting trenches and bunkers—and to his surprise, washing lines fluttering with white clothes. The sight spoke volumes about the 2 Royal Scots' lax discipline. Then fog descended, reducing visibility to barely twenty meters, and heavy rain obscured the landscape. Doi sensed a fleeting advantage but lost radio contact with his battalions. Worse still, the Shing Mun Redoubt lay outside his regimental boundary, making an unauthorized attack a serious breach of protocol. When his units finally arrived, supporting artillery remained unavailable, held up by demolished roads. Doi chose to attack anyway. Doi ordered the 2nd Battalion to scout the eastern approach while the 3rd Battalion spearheaded the main assault. Major Nishiyana commanded the 3rd, sending Lieutenant Wakabayashi and Kasugai with 150 volunteers to lead. The Shing Mun Redoubt itself comprised five concrete pillboxes and an artillery observation post, connected by underground tunnels named after London landmarks—Piccadilly, Haymarket, and others. The whole complex sat on small rises above Jubilee Dam, then the British Empire's tallest. Defending the redoubt was A Company, 2 Royal Scots, under Captain Jones—just three officers and 39 men. One platoon of 25 soldiers under 2nd Lieutenant Thompson held the forward positions. Against this stood 150 Japanese attackers, with another 1,500 troops in reserve. Battalion orders forbade using the concrete works as fighting positions; they were meant for the Vickers machine-gun team and artillery protection. No mines could spare the redoubt's perimeter. On the night of the 9th, roughly half the defenders sheltered in the observation post while the remainder manned pillboxes 401b and 402. At 2000 hours, Thompson led a nine-man patrol northward to check Needle Hill and Shing Mun Valley. Despite two and a half hours in the field, the patrol returned at 2220 hours without detecting the Japanese force massing just 500 meters away. By that time, Lieutenant Kasugai's assault group had crossed Jubilee Dam undetected, bypassed unmanned Post X, and assembled below Pillbox 401b. At 2200 hours, Japanese sappers cut through two lines of wire. At 2300—H-Hour—Lance Corporal Laird heard movement in the bushes and opened fire. The Japanese answered with grenades and rushed the pillbox lines. One team entered the tunnel system; another hurled grenades through the air vents. Corporal Campbell swung his Vickers toward Pillbox 401, catching Japanese headquarters personnel in the open. Sergeant Robb mustered a counter-attack force of thirteen men. Five fell as casualties—Laird, Basnett, Coyle, Casey, and Jardine. Casey was killed; only Jardine escaped. The alert triggered gunfire from the observation post's defenders, and Forward Observer Wilcox called down 4.5-inch and 6-inch artillery fire. Without strong reinforcement and leadership, four Royal Scots and an Indian sentry abandoned the northern positions and withdrew to the observation post. Captain Jones received orders from Brigade to counter-attack, but delegated the task to Thompson. As Thompson attempted to leave the observation post, he found the gate locked from outside. This calamity had a backstory: just before the attack, Thompson had admitted a civilian at the request of Force 163 (SOE's sabotage unit in Singapore). Private Wylie had taken the observation post key from Lance Naik Kishan Singh and, upon exit, locked the gate behind him. The three officers were now literally locked out of the battle. Second Lieutenant Mochizuki and his platoon infiltrated through open trenches and began attacking the observation post from above, attempting to breach the hatches. The defenders held for three hours before Japanese sappers forced explosives down the air vents. At 0230 hours, the blast killed two Indian signallers inside. Thompson sustained a severe eye wound from grenade shrapnel; Jones and Warrant Officer Mead were both wounded. The remaining fifteen defenders surrendered; eleven became casualties. Meanwhile, a Rajput patrol engaging Japanese forces in the river valley managed to drive them back toward the redoubt. One final pillbox endured for another eleven hours before suffering the same sapper treatment. By 0400 hours on 10 December, the entire redoubt was lost. For just two killed, the Japanese had achieved a decisive victory—the breakthrough that would shift the entire campaign's trajectory. By dawn, a Japanese flag flew triumphantly from Point 255.   Major-General Maltby's assessment was stark: "This was calamitous, for Shing Mun was the key to the whole of the left position." Yet the Japanese perspective differed sharply. Having achieved a breakthrough, Colonel Doi received astonishing orders to withdraw—his success had transgressed into the 230th Regiment's sector and violated the meticulously prepared war plan. Doi refused. After heated confrontation with Major-General Ito and Lieutenant-General Sakai, his insubordination was later censured, though a divisional staff officer, Oyadomani, bore the scapegoat's burden of rebuke for failing to restrain Doi's initiative. With Shing Mun secured, Doi methodically fed reinforcements into the position. Throughout December 10th, the Japanese remained uncertain of their advantage and adopted a cautious strategy, probing British lines to exploit the evident disorder. Brigadier Wallis ordered the Royal Scots to mount an immediate dawn counterattack supported by the Rajputs and artillery. Lieutenant-Colonel White demurred; his battalion lacked any reasonable prospect of success. The resulting counterattack proved half-hearted at best. At first light on December 11th, the Japanese pressed Golden Hill forcefully, only to be repulsed by Captain Newton's Rajputs and gunfire from HMS Cicala. White positioned D Company in shallow shell scrapes and rusted wire—inadequate defenses against Japanese assault. Captain Pinkerton led a desperate bayonet charge that morning, temporarily clearing the Japanese and enabling the evacuation of wounded. Yet B, C, and D Companies absorbed devastating losses; Captains Rose and Richardson fell commanding their positions. With the Royal Scots collapsing, X Company of the Winnipeg Grenadiers and armored support rushed westward to seal the widening gap along Castle Peak Road. Meanwhile, Maltby grappled with alarming reports: Japanese landings on Lantau Island southwest of Hong Kong. Heavy artillery drove them back. A second probe at Aberdeen, where Special Naval Landing Forces approached within 300 yards of the naval base, was repelled by Canadian machine-gun fire. In hindsight, these were mere feints—never intended as the primary thrust. The stratagem succeeded. Maltby retained his island reserves—the Middlesex and two Canadian battalions—precisely where they would be needed. Had Wallis executed a forceful counterattack on December 10th with the Royal Scots and brigade reserves, the Rajputs might have retaken Shing Mun while the Japanese remained disorganized. Instead, Colonel Doi faced a furious Sakai, who had flown down from 38th Division headquarters at Taipo for what amounted to a pre-court-martial examination. The moment passed. After only 48 hours of combat, Maltby concluded the mainland was indefensible. The three battalions would withdraw intact to Hong Kong Island to assume their fortress defense roles. The naval commodore objected—demolitions, supply transfers, and troop ferrying required more time. The evacuation order was deferred 24 hours until noon on December 11th. At that hour, the decision was finalized: the mainland would be abandoned under cover of darkness. Further demolitions followed—the China Light and Power Station, the cement works on Tsing Yi Island, the docks. Yet Brigadier Wallis believed this merely a precautionary measure; he anticipated holding the mainland for at least another week. The withdrawal avoided Kowloon's congested streets, where street fighting and fifth columnists might have entrapped the British—a calculation that proved prescient, as an IJA advance column of 350 men from the 3/230th infiltrated Kowloon on the morning of December 12th to cut off the retreating garrison. The Royal Scots and Canadians retired south to Shamshuipo Barracks and Jordan Road Pier in western Kowloon; the Rajputs and supporting gunners withdrew eastward to Ma Lau Tong, which anchored the fortified Devil's Peak Peninsula. Maltby intended to hold this commanding position permanently—it was easily supplied across the narrow Lyemun Strait. The evacuation proceeded largely as planned. The Canadians and Royal Scots embarked on the afternoon and evening of December 11th under Punjabi covering fire. The Punjabis faced a harder journey—a moonlit scramble along steep Kowloon hills laden with supplies but critically short of mules. One group lost its way at a crucial junction, splitting into two columns. One proceeded toward Devil's Peak as planned; Battalion Headquarters descended into Kai Tak Airport, threaded through Kowloon City, and was evacuated by Star Ferry at Tsim Sha Tsui, fighting until the final moment as the last man boarded. The Rajputs' commander, unable to reach his Punjabi counterpart, abandoned Ma Lau Tong for the closer Hai Wan line near Devil's Peak. By 0400 hours on December 12th, the Punjabis and one Rajput company, accompanied by the gunners of the 25th Medium Battery, crossed Lyemun Strait to Hong Kong Island. Morning evacuations continued with minimal Japanese interference—a surprising restraint. Naval support proved crucial. The withdrawal completed by the morning of December 13th, though 170 mules were lost to the desertion of Chinese laborers—a deprivation that would haunt the subsequent campaign. During this passage, the IJN attempted shore bombardment. The Japanese cruiser and supporting vessels found themselves outranged by the defenders' 9.2-inch guns. The damage inflicted proved sufficient to discourage future naval adventures; for the remainder of the battle, the IJN maintained a cautious distance and played only a peripheral role. The speed and execution of the withdrawal apparently caught the Japanese unprepared. Their most probable expectation had been a full-scale assault on Devil's Peak accompanied by heavy bombardment—a day earlier, an assault without artillery preparation had been driven back with severe losses. The British appeared stronger than anticipated. The defense of the mainland had lasted only five days. With the mainland secured, the Japanese pivoted toward Hong Kong Island. The first move was diplomatic rather than military. At 0900 hours on December 13th, a small boat bearing a white flag crossed the harbor. It carried Colonel Toda, Lieutenant Mizuno, Mr. Othsu Dak, and two European women—the prominent and heavily pregnant Mrs. Macdonald and Mrs. C. R. Lee, wife of the Governor's secretary, accompanied by her two dachshunds. The surrender terms were flatly rejected. Major Boxer delivered the written refusal personally. Mrs. Macdonald remained in Hong Kong to give birth; Mrs. Lee and her dogs returned to Kowloon. The Japanese commenced systematic shelling of selected targets. On December 13th, a 9.2-inch gun on Mt. Davis was silenced; Belcher's Fort sustained damage. The following day, a 3-inch mount on Mt. Davis was hit, killing Chinese gunners and triggering morale collapse; some deserted. On December 15th, the barrage shifted to the northern shoreline, deliberately targeting pillboxes. At 2100 hours that night, approximately three Japanese companies attempted crossing the harbor in rubber rafts toward Pakshawan but were driven off by machine-gun fire. Fifth columnists intensified their operations—signaling with mirrors, sniping from concealed positions, spreading propaganda to encourage Chinese desertion. Their impact might have been catastrophic had not Rear-Admiral Chan and Colonel Yee, armed with tommy guns and grenades, systematically eliminated many of these saboteurs. By December 16th, half the pillboxes between the racecourse and Lyemun Strait lay destroyed. The intended crossing site was unmistakable. On December 17th, the Japanese dispatched a second peace mission—Colonel Toda's team now accompanied by Mrs. Lee with her dachshunds and a pregnant Russian woman. Again, the overtures were rebuffed. On December 18th, the bombardment reached new intensity. Oil storage tanks ignited, sending dense black smoke cascading across the northeast corner of the island—perfect concealment for what was to come. All signs pointed to imminent assault.  I want to note something here, you sometimes hear about how race played a role in the Pacific War and indeed it did. Racist attitudes were found on both the western and Japanese side of the war. There is a fantastic book dedicated to this by the way called, War without Mercy by John Dower, highly recommend it. What is interesting is how these racial attitudes actually affected decision making during the war. Take General Sakai's night amphibious assault for example. Western soldiers such as the Canadians in Hong Kong reported they were told by their commanders that quote "Don't worry about the Japanese, they can't see at night, they all wear glasses" another Canadian soldier wrote in a diary "They don't see well, especially at night we knew this as a matter of fact." Indeed the week before the outbreak of the battle of Hong Kong, Canadian officers attending a briefing by a British officer were informed that "Japan's aircraft were mostly obsolete, its air force had little practice in night flying, and its pilots were myopic and thus unable to carry out dive-bombing attacks". You might be asking yourself, why did they think the Japanese were myopic? The scientific explanation of the day was this "That the Japanese suffered widespread inner-ear damage. What caused this? Japanese motherhood, the practice of strapping babies to their mothers backs, it was explained caused their heads to bounce about, like when they harvested rice and permanently impaired their sense of balance". I particularly love this quote, one of my professors dedicated an entire class to the bizarre racial attitudes before and after the pacific war. There was a belief the Japanese could not see well at night, many western air force commanders actually believed there were times of day like at dawn or dusk where Japanese pilots would not operate because of this hindrance. American military commentator Fletcher Pratt in 1939 analyzed Japan's military strengths and explained it had four major weaknesses. The first was scarcity of resources such as iron, and steel. The second was that the Japanese "can neither make good airplanes nor fly them well" as a result of " the Japanese as a race have defects of the tubes of the inner ear, just as they are generally myopic. This gives them a defective sense of balance, the one physical sense in which an aviator is not permitted to be deficient.". The third was that the Japanese were short-tempered and were inclined to waste men unnecessarily when operations did not go well. The fourth was that the Japanese would simply never date to provoke a war with the US. Again Mr Pratt was an American military commentator whose information came from multiple accounts given by high ranking military officials! The British military maintained that the Japanese avoided night operations on land because they were simply incapable of caring them out well, while they avoided nighttime aerial attacks, as well as dive-bombing for the same reasons Pratt said quote "their pilots were poor and their aircraft inferior.". So take this all to mind when I talk about how certain ranking officers go about making their decisions. By the evening of 14 December, evacuated mainland units had regrouped on Hong Kong Island. The defensive structure was restructured into two brigades: Brigadier Wallis commanded the East Brigade from Taitam Gap, while Brigadier Lawson headed the West Brigade from Wongneichong Gap. This reorganization placed the 5/7 Rajputs across the north-east perimeter, defending over 3,500 metres of coastline with B Company positioned on the hills behind Taikoo Docks. The Royal Rifles of Canada held the north-east corner from Stanley to points south, with reserve positions at Lyemun. Two HKVDC companies supported from Taitam Valley and Pottinger Gap, while the veteran Hughsiliers manned the North Point power station. British and Canadian artillery provided fire support across the northern approaches. The West Brigade held the north-western and south-western shores. The 2/14 Punjab stretched from Causeway Bay to Belcher's Point, protecting Government House and General Maltby's headquarters, with battalion headquarters at Mid-Level. The Winnipeg Grenadiers covered the south-west from Wanchai Gap, with one company detached to the brigade headquarters at Wongneichong Gap. The 2 Royal Scots, severely mauled at Shing Mun, were held in reserve with fresh replacements from the HKVDC. The Middlesex Regiment's companies were dispersed along 72 pillboxes encircling the entire shoreline, while an improvised Z Company of clerks, musicians and cooks defended battalion headquarters at Leighton Hill. Supporting artillery included 9.2-inch and 6-inch guns on Mt Davis and 4.7-inch guns at Belcher's Point. As darkness fell on 17 December, Japanese reconnaissance swimmers reached Taikoo, confirming the crossing site despite detection. Reinforcements arrived: 18 additional heavy bombers from Guangzhou and 26 fighters from Taiwan joined the aerial assault. The crescendo of artillery fire on the 18th, combined with masses of small craft gathering across the harbour, signalled the imminent invasion. Japanese divisional headquarters shifted to Ma Tau Wei near Kai Tak Airport; the 23rd Army moved to Taipo. The invasion force disposed itself with the right flank embarking from east and west of Kai Tak, the left from Devil's Hill area. At 1800 hours, the invasion order was issued; by 1930 hours Sakai himself arrived in Kowloon to oversee the crossing. At precisely 2000 hours, the 2/228th Regiment silently paddled their collapsible craft toward Taikoo Docks and the nearby sugar factory as artillery support commenced. Colonel Doi embarked with 80 headquarters personnel aboard a large barge, leading the second wave. The conditions favoured the invaders—a moonless night, sporadic showers, and thick black smoke from burning oil tanks obscured their movement. They reached the midpoint undetected before searchlights and machine-gun fire scattered the small boats, separating commanders from their units. Despite this chaos, the assault pressed forward. At 2140 hours the artillery shifted to deeper targets; at 2145 hours the first wave of 3/230th landed at North Point, followed minutes later by 2/228th and then 2/229th. Three red flares signalled a successful landing. All six Japanese battalions were ashore by midnight, though wire fencing delayed their advance and Bren carriers added to the confusion on the beach. General Sano arrived at Taikoo one hour later. The 5/7 Rajputs bore the initial brunt of the assault. Despite days of preliminary shelling, the Rajputs mounted a stubborn defence; Japanese casualties mounted rapidly. The anti-tank company suffered so grievously that only a single gun remained operational. The Japanese plan was tactical rather than frontal—bypass isolated positions for later elimination and push rapidly toward high ground. Overwhelmed by numerical superiority and losing most of their officers, Rajput resistance gradually crumbled. Contradicting all evidence, Maltby remained convinced the main Japanese assault would strike directly at Victoria from Kowloon. He therefore withheld the counter-attack, instead dispatching a platoon of Middlesex troops, Royal Marines, naval personnel, and the Rajputs' reserve company—supported by artillery—to establish a defensive line blocking the Japanese advance toward Central District. Three HKVDC armoured cars were sent to protect East Brigade headquarters; two more reinforced the Middlesex battalion headquarters. It was manifestly inadequate against six Japanese battalions. In post-war dispatches, Maltby conceded his grave miscalculation: he believed he faced at most two battalions, not a full division. As the 3/230th broke through D Company of the 5/7 Rajputs, the 2/230th came ashore nearby and wheeled westward toward Victoria. Their advance halted abruptly at the North Point power station, held by an extraordinary force: four officers and 36 men of the HKVDC Hughsiliers Platoon—alongside 39 civilian volunteers from China Light & Power and Hong Kong Electric, including the station manager Vincent Sorby—plus eight Free French soldiers commanded by Captain Jacques Egal, a Shanghai businessman turned soldier. Without exception, these men were World War I veterans, many aged 55 or older. Some had fought in the Boer War. Major J. J. Patterson, the Hughsiliers' commander, had served with Allenby's Camel Corps and been mentioned in dispatches six times; he was chairman of Jardine Matheson and a legislative councillor. Private Sir Edward Des Voeux, 8th Baronet, a millionaire bullion trader and secretary of the Hong Kong Club, had enlisted as a private when deemed too old for mobilization. Private T. A. Pearce, 67 years old, was chairman of J. D. Hutchison & Company and secretary of the Hong Kong Jockey Club. Captain Bruch, the second-in-command, aged 60, chaired a major trading house. The list extended through Hong Kong's business elite, each choosing to fight rather than flee. These old men held an entire regiment at bay. When Patterson called for aid, Maltby dispatched an HKVDC armoured car with a platoon from Z Company, 1 Middlesex, but the reinforcement was ambushed before reaching the station; only nine soldiers, including 2nd Lieutenant Caruthers, broke through. Unable to dislodge the defenders by assault, the Japanese resorted to bombardment. By 0145 hours on 19 December, surrounded and ammunition exhausted, the defenders withdrew to King's Road behind a disabled bus. The Japanese deployed three machine guns, killing or mortally wounding all but Private Geoghan. A Japanese officer, believing all dead, cautiously approached—only to have Geoghan, despite his wounds, surge from cover and charge. He killed the officer and four soldiers before collapsing from wounds. Somehow Geoghan survived. Those old men—civilians and reservists, businessmen and veterans—held the entire western Japanese advance for 18 hours, buying Maltby precious time to withdraw forces and establish a new defensive line. Their stand at North Point became one of the campaign's most improbable acts of defiance. As the 2/229th Regiment advanced southward toward Mt Parker, the 3/229th landed at Aldrich Bay and turned eastward toward Lyemun, where they seized an HKVDC 6-inch gun. Major Bishop of the Royal Rifles of Canada, unaware of the landings, dispatched 15 Platoon under Lieutenant Scott to check on A Company of the Rajputs. Unexpectedly, they encountered armed men in civilian clothes at Lyemun's gate—the first indication of the Japanese presence on the island. Yet senior officers dismissed initial reports of landings and lost positions as exaggerated, a critical miscalculation that proved catastrophic. At Lyemun Fort, the 5th Anti-aircraft Battery of the HKVDC was caught completely unaware and surrendered after minimal resistance. The Japanese then committed one of many atrocities that would characterize the battle, bayoneting 29 survivors. Alerted by 15 Platoon's report, Bishop attempted a counter-attack with two platoons to retake the fort, but the defenders could not scale the six-meter wall and withdrew with nine men killed. As the 2/229th pressed southward toward Mt Parker, they overran the Salesian Mission, which had been converted into an Advance Dressing Station. No. 8 Company of the 2/229th systematized a massacre of medical personnel and the wounded—an atrocity that would later become pivotal evidence at Colonel Tanaka's war crimes trial. Yet amid the chaos, several medical staff and wounded survived to testify: Gunners Y. K. Chan and Martin H. C. Tso of the 5th Battery, Captain Osler Thomas of the HKVDC, Captain Martin Banfill of the Royal Canadian Medical Corps, and Corporal Norman Leath of the Royal Army Medical Corps. C Company of the Royal Rifles, reinforced with captured Japanese machine guns abandoned by fleeing Rajputs, inflicted heavy casualties on the attackers, with one Japanese company suffering over 65 percent losses. Yet overwhelmed by superior numbers and suffering nearly two platoons worth of casualties themselves, the Canadians were forced to withdraw, leaving only a reinforcement platoon under Lieutenant Blaver on Mt Parker. When Captain Clerke arrived at dawn with 16 Platoon to reinforce the position, he found over 100 Japanese already entrenched. With no artillery support and only two platoons available, Clerke had no choice but to withdraw, abandoning Mt Parker to the enemy. By dawn on the 19th, the East Brigade faced catastrophe. Infantry strength had been halved, and losses of artillery were equally devastating. A series of misunderstandings and misinterpreted orders led coastal gun crews at Capes D'Aguilar and Collinson to destroy their own guns in confusion or lose them to the advancing Japanese. Of the entire artillery complement, only one 18-pounder and two 3.7-inch field guns remained operational. Wongneichong—literally "yellow muddy creek" in Chinese—occupies the center of Hong Kong Island, a valley separating the eastern hills (Mt Parker, Mt Butler, Jardine's Lookout) from the western massif (Mt Nicholson, Mt Cameron, Victoria Peak). This narrow gap formed the critical artery linking northern and southern Hong Kong, making Brigadier Lawson's decision to place West Brigade Headquarters there strategically sound yet tactically vulnerable. Recognizing the exposed position after the landings, Major Lyndon had located an alternative site south of Mt Nicholson, scheduled to open the following day. In response to the Japanese landings, Lawson dispatched three platoons of Winnipeg Grenadiers to block the advance. Lieutenant C. D. French took 18 Platoon to Mt Butler, Lieutenant G. A. Birkett's 17 Platoon moved to Jardine's Lookout, and Lieutenant L. B. Corrigan's platoon took the north-west approach to the Gap. As on Mt Parker, both French and Birkett were killed almost immediately, their small forces overwhelmed by battalion-strength enemy forces with artillery support. By morning on the 19th, two battalions each of the 230th and 228th Regiments were pushing for the high ground. The 2/230th rapidly occupied Jardine's Lookout while the 3/230th moved to Mt Nicholson and the 2/228th advanced toward the Gap itself. No. 3 Company of the HKVDC under Major E. Stewart held pillboxes around Jardine's Lookout, while No. 1 Company under Captain Penn defended Taitam Valley with forward positions at Quarry Gap. The pillboxes, despite heavy bombardment, held longer than anticipated—some for as long as 24 hours. Stewart maintained his company headquarters until the 22nd, four days of continuous fighting without adequate ammunition, food, or water. No. 3 Company suffered 80 percent casualties and ceased to exist as a cohesive unit. Realizing that the enemy had penetrated to Mt Butler, Brigadier Lawson decided counter-attack was essential. Major A. B. Gresham of A Company, Winnipeg Grenadiers, was ordered to retake Mt Butler and clear Jardine's Lookout. The Canadians initially succeeded, with Warrant Officer 2 J. R. Osborne leading a bayonet charge, but a strong counter-attack by three Japanese companies drove them back by 2200 hours. The Canadians held firm until 1500 hours, when Gresham attempted surrender despite waving a white flag—the Japanese shot him regardless. In the chaos that followed, a Canadian killed a Japanese officer in response, triggering a grenade barrage. Warrant Officer Osborne caught the grenades and hurled them back as fast as they came, but one landed in an impossible position. Without hesitation, Osborne threw himself on the grenade to save his comrades. This act of supreme self-sacrifice earned him a Victoria Cross—the first Canadian VC of World War II. At dawn on the 19th, Maltby dispatched A Company of the 2 Royal Scots under Captain K. J. Campbell to reinforce the Gap, but they lost all their officers with only 15 men reaching Lawson's headquarters. A group of sailors under Commander A. L. Pears came from the south, but were ambushed en route; only a few reached the Postbridge—a house just south of the Gap—which became an isolated strongpoint. The 3/230th captured the police station perched on a mound at the southern point of the Gap, then swept eastward to seize the anti-aircraft guns of 7th Battery (barely 250 meters from brigade headquarters) and continued uphill toward Taitam Hill to capture additional 6-inch and 3.7-inch guns. Maltby's response was to call down final protective fire from the HKSRA in Happy Valley, inflicting massive casualties on the 3/230th. At 1000 hours on the 19th, Lawson reported that his headquarters was surrounded and he was about to "go outside and shoot it out." Captain H. A. Bush provided covering fire as Lawson and his entire staff, including Middlesex signallers, were cut down by machine-gun fire across the Gap. Lawson's body was discovered two days later halfway up the hill behind his headquarters, dead from a gunshot wound to the thigh. Lieutenant Kerfoot of the 2/14 Punjab with his three Bren carriers arrived minutes too late to save him. IJA who saw the fight made a note stating "he died heroically". At 1330 hours on the 19th, Maltby issued "Operation Order No. 6" calling for a massive counter-attack to commence 90 minutes later. The plan was ambitious: A and D Companies of the 2/14 Punjab were to attack east from Victoria to North Point to relieve the Hughsiliers, while HQ Company of the Winnipeg Grenadiers and the 2 Royal Scots were to attack the Gap and Jardine's Lookout. Eight field guns were promised but never materialized. There was no reconnaissance, no proper orders, and minimal coordination. The Canadians were to rendezvous with the 2 Royal Scots at Middle Gap at 1530 hours, but the Scots were late; the Canadians advanced without them. Captain Pinkerton of D Company, 2 Royal Scots, was ordered at the last minute to attack up Wongneichong Gap Road—the same route A Company had taken only seven hours earlier and been decimated—because it was reported as "lightly held." Captain A. M. S. Slater-Brown led the advance with three Bren carriers and attached engineers, followed by D Company of the Winnipeg Grenadiers. Just as the carriers reached the burnt-out trucks of A Company, they too were ambushed. Mortars and machine-gun fire rained down from Jardine's Lookout, where the Japanese occupied captured HKVDC pillboxes. Slater-Brown and 2nd Lieutenant Bell, the battalion intelligence officer, were killed instantly. The Royal Scots waited in the roadside ditches until 0200 hours before launching an attack on the police station, reaching the steps before Pinkerton was severely wounded. Captain Ford's composite company attack at 0300 hours also failed. Simultaneously, C Company under Lieutenant F. L. Stanier attacked Jardine's Lookout—also unsuccessfully. This series of piecemeal assaults cost the Royal Scots eight officers and 68 soldiers. Growing increasingly desperate, Maltby ordered Major Hodkinson, Officer Commanding HQ Company of the Winnipeg Grenadiers, to attack the Gap and proceed to Mt Parker. Requesting reinforcements from A Company and the Royal Scots, Hodkinson launched his assault with covering fire from Lieutenant Corrigan on Mt Nicholson. The Grenadiers managed to reach Mt Nicholson with only five unwounded men, though Corrigan's group advanced to within 300 meters of the Gap road. Then came an unexpected stroke of fortune: while skirting Mt Nicholson, Hodkinson's group stumbled upon approximately 500 Japanese—probably from the 3/230th—eating lunch without posting sentries. The Winnipeg Grenadiers unleashed devastating fire, inflicting heavy casualties. By 1745 hours, Hodkinson's force was only 100 meters from Repulse Bay Road. With four men and a 2-inch mortar, Hodkinson worked his way to the abandoned brigade headquarters, bringing 20 walking wounded with him. He established a foothold at the Gap, receiving orders to attack the police station at 2200 hours. Two HKVDC armoured cars with Vickers machine guns came in support, but accurate enemy fire disabled both. Like Pinkerton before him, Hodkinson reached near his objective before being severely wounded. For this action of determination and courage, Hodkinson was awarded the Distinguished Service Order. By the morning of the 20th, the Japanese had consolidated control of the Gap, but elements of D Company, Winnipeg Grenadiers, still held various pillboxes north of the position. Initially, 17 and 18 Platoons occupied forward positions with Company Headquarters and 16 Platoon in reserve, positioned across Wongneichong Road almost directly opposite brigade headquarters. Forward positions were rapidly overrun and cut off from the company. They fought without officers until withdrawal to battalion headquarters became necessary. With Captain A. S. Bowman killed and Captain R. W. Philips wounded, command fell to Lieutenant T. A. Blackwood. An attempt by B Company to relieve D Company on the evening of the 20th failed catastrophically, killing all officers and 29 men. With fewer than 50 men remaining—all wounded—Lieutenant Blackwood continued inflicting heavy casualties on the Japanese until 22 December, when exhaustion and the complete absence of ammunition, food, and water forced surrender. D Company killed over 200 Japanese soldiers. Post-war Japanese reports refused to believe that a single company had held the Gap for so long. For their stubborn defense, Philips and Blackwood were awarded the Military Cross, and three enlisted men received the Military Medal. The battle of Wongneichong Gap was characterized by fragmented planning and poor coordination. On the night of the 19th-20th, no fewer than three separate companies attacked the police station, with another company assaulting Jardine's Lookout—all within five hours, all independently organized by local commanders. Had these disparate actions been coordinated into a single simultaneous assault, fortress headquarters' objectives might well have been achieved. By the end of the 20th, the British had lost the vital ground of Wongneichong, though Postbridge House remained as an isolated holdout. For the Japanese, Wongneichong became the single costliest engagement since the invasion began, with over 800 casualties and the regimental commander of the 3/230th seriously wounded. I would like to take this time to remind you all that this podcast is only made possible through the efforts of Kings and Generals over at Youtube. Please go subscribe to Kings and Generals over at Youtube and to continue helping us produce this content please check out www.patreon.com/kingsandgenerals. If you are still hungry after that, give my personal channel a look over at The Pacific War Channel at Youtube, it would mean a lot to me.   Japanese landings on 18-19 December overwhelmed the East Brigade. Brigadier Lawson died defending the vital Gap. Desperate, uncoordinated counter-attacks by British and Canadian troops failed to dislodge the enemy. D Company Winnipeg Grenadiers held the position for four days against overwhelming odds before surrendering. The Japanese suffered 800 casualties but secured this critical strategic ground.

Dog training motivation is not simply about how quickly a dog performs or how intensely they pursue a reward. Does your dog genuinely enjoy training, or have they simply learned to chase the food, ball or tug at the end of it? The difference matters. When the reward is the only valuable part, performance can become dependent on what the dog can see, while the training itself holds very little value. In Episode 372 of The Canine Paradigm, Glenn Cooke and Pat Stuart explore dog training motivation, what actually matters to the individual dog, and how handlers can build training that dogs actively choose to participate in. What Actually Matters to Your Dog? Owners can easily assume they know what their dog values. Some dogs are strongly motivated by food, while others place greater value on play, movement, social interaction, environmental access, relief from pressure or the opportunity to perform the work itself. Understanding dog training motivation requires us to observe the individual dog rather than decide what should motivate them. A reward is only reinforcing if it genuinely increases or strengthens the behaviour that preceded it. Creating Dog Training Motivation That Lasts Creating value in the game means allowing the handler, cues, equipment and opportunity to work to predict worthwhile outcomes. Sessions should be clear, rewarding and short enough to preserve the dog's enthusiasm. Visible lures should gradually disappear. Reward placement and timing should support the next repetition, while difficulty should increase without producing constant failure. The aim is a dog that engages before seeing the payment, persists when challenged and recovers comfortably from mistakes. For sporting and working dogs, intensity alone is not enough. Strong motivation also includes clarity, connection and the ability to settle after the work finishes. Fair pressure and well-timed correction can exist within balanced training, but they cannot compensate for confusing criteria or a game the dog was never taught to value. Key Takeaways Motivation is individual and can change with the situation. Reinforcement is defined by its effect on behaviour, not the handler's intention. A visible reward can support learning, but it should not become the dog's only reason to perform. Genuine engagement includes enthusiasm, clarity, resilience and connection with the handler. The goal is to create a dog that chooses the training game, even before the payment appears. Frequently Asked Questions Does using food mean my dog only works for food? No. Food can help establish a strong reinforcement history. The aim is to prevent the dog from becoming dependent on seeing the food before performing. How can I tell whether my dog enjoys training? Look for voluntary engagement, anticipation, resilience after mistakes and a willingness to participate when the reward is not visible. Can corrections reduce a dog's motivation? Poorly timed, confusing or excessive corrections can create conflict. Fair and understandable communication should clarify the task without overwhelming the value already built into the activity. Further Details For practical training education and foundational health resources, visit our Dog Training and Canine Wellness page. It brings together Serious Dog Business and Canine Ceuticals for dog owners, trainers and handlers. Support The Canine Paradigm and access exclusive content through our Patreon page. Every contribution helps us continue producing the podcast. Explore our complete range of merchandise at our Teespring store. You can also support the podcast by sharing episodes, recommending guests, leaving reviews, and spreading the word within the canine community. For podcast listening options, visit our Subscribe to Podcast page and choose the platform that works best for you. Subscribe to The Canine Paradigm on YouTube for video content, updates, and additional discussions. If you enjoy the podcast, please consider leaving a review on Apple Podcasts, Spotify, or any other podcast directory you use. Reviews help more dog owners, trainers, and canine professionals find the show. Support Our Supporters Narelle Cooke hosts Natural Health for People and Pets, available on major podcast platforms. For premium human-grade canine supplements, visit Canine Ceuticals. Canine Ceuticals is also now available in the USA. Jason Firmin of Einzweck Dog Quip is another proud supporter of the show. For daycare and heartfelt training services, check out From the Heart Dog Training. Our dear friend and regular contributor, Birdy O'Sheedy, can be found at The Magic in Dogs. Special Thanks A huge thank you to all of our contributing artists. Please take a moment to support their work. Jane StuartAvery KellerZoie Neidy

My guest today is legendary, bestselling author Steven Pressfield, who's known for his work on creativity, self-discipline, and how to overcome the inner force of what he calls, "capital R" - Resistance.He's written 25 books including the wildly popular - The War of Art, Turning Pro, Do The Work, and Put Your Ass Where Your Heart Wants to Be - which have influenced tons of successful writers, thinkers, artists and entrepreneurs - including Ryan Holiday and Rich Roll.Steven wrote for 27 years before his first novel The Legend of Bagger Vance got published and made into a Robert Redford film starring Matt Damon and Will Smith. He's also written a memoir Gov Cheese and many historical novels like, A Man at Arms, Gates of Fire, and his latest, The Arcadian!I've been reading and recommending his work for years, so to have him on the show felt like such a full-circle, meant-to-be moment!Here's some of what Steven and I dive into in this episode:Resistance (with a capital R):That invisible, diabolical force that shows up every time you try to write the book, record the podcast, go to the gym, leave the job, or step toward your calling.“Resistance will kill you”:Steven shares why not following your creative or spiritual calling doesn't just make you uncomfortable… it can slowly kill your spirit, your self‑trust, and your life-force.Self‑​Discipline & ​Ritual:We talk about why discipline gets such a bad rap, how it actually becomes an act of self‑love, and what it looks like in real life to “put your ass where your heart wants to be.”Art as a Spiritual Practice:Steve and I dig into the idea that we're spiritual beings in a messy, beautiful, often painful world – and how making art (in any form) is one of the most powerful ways we can respond to and answer that.His new book, The Arcadian:He shares the 40‑year journey behind this story, karma, warriors, and what it costs a soul to live a life of violence… and try to find redemption.I also share a bit about my own writing journey with my memoir, and how bumping up against Resistance has pushed me deeper into my spiritual practice and creative life.This episode is for you if:

Send us Fan MailABA on Tap is proud to present Mark Malady (Part 2 of 2):If you have ever felt pressured to list “behaviors for reduction” even when you know the real need is skill building, this conversation will hit home. ABA on Tap continues our sit down with Mark Malady, to talk about constructional ABA, where the aim is not to stop behavior for everyone else's comfort but to build the repertoires that let a learner get their needs met with more freedom, safety, and dignity.We get into why procedure-heavy programming can create rigid learning that looks great on paper but does not generalize in real life. From joint attention and enriched environments to response classes and functional outcomes, we explore how to stop obsessing over response form and start focusing on what the behavior does. That includes a candid take on eye contact goals, developmental milestone traps, and why “critical periods” matter less than building the component skills that support informed choice.We also unpack process-based practice and measurement. Mark shares how process can still be measurable and accountable, why clinicians may need to make many small adjustments in a single session, and how low-dose models can outperform massive hour prescriptions when targets are chosen well. You will also hear about the Generate skill-based assessment, milestone benchmarking, and a powerful example of “dignity of risk” where opportunity, not skill deficit, is the real barrier.If this episode sharpens your thinking, subscribe for more conversations that challenge default ABA habits, share it with a colleague, and leave a review so more clinicians and families can find the show. What would you stop targeting tomorrow if you shifted fully to function and quality of life?Tune In, Drink Up, and ALWAYS ANALYZE RESPONSIBLY.Support the show

Send us Fan MailABA on Tap is proud to present Mark Malady (Part 1 of 2):A piece of duct tape on the floor becomes a turning point for how we think about safety, reinforcement, and basic human connection. Mike and Dan sit down with Mark Malady to trace the long arc from an IEP and constant school changes to building programs for adults with severe aggression and minimal communication. Along the way, Mark shares the moment he stopped accepting the “he doesn't like people” assumption and started asking a better behavioral question: what history built this repertoire, and what would it take to build a new one?ABA on Tap and Mark give a full pour of the ideas that shape real clinical work. Mark connects early experience in adult disability services to big-picture themes in applied behavior analysis: functional communication, humane reinforcement, and why our field too often mistakes procedural compliance for effective intervention. He breaks down process versus procedure orientation in plain language, ties it to ACT and relational frame theory, and explains why relationship quality often predicts outcomes better than which “manual” you follow.Finally, we get honest about the hard parts of ABA in autism services and school settings: power dynamics, “firefighting” models that wait for crisis, and resource decisions that prioritize deceleration over skill acquisition. If you care about ethical ABA, assent and consent, better assessment practices, and building meaningful skills across the lifespan, this conversation will challenge you in the right ways. Subscribe for Part 2, share this with a colleague, and leave a review with the biggest question you want our field to answer next.Tune in, drink up and ALWAYS ANALYZE RESPONSIBLY.Support the show

Reinforcement learning from verifiable rewards (RLVR) is the hot new thing in LLM training. It's so hot, and people spend so much time talking about it, that they sometimes lose sight of the big picture. Stepping back, LLMs can do lots of very impressive things. How? Where did those capabilities come from? Fundamentally, they come from a combination of: (1) Imitative learning, including pretraining and supervised fine-tuning (SFT) See my earlier discussion: “LLM pretraining magically transmutes observations into behavior, in a way that is profoundly disanalogous to how brains work”.(2) Reinforcement learning, including RL from human feedback [RLHF], RL from AI feedback [RLAIF], and especially RLVR.[1] If we look at the final trained LLM, we can ask how important each of those two pieces was, in explaining the LLM's capabilities. And my claim is that it's way more (1) than (2). I'll start in §1 with some relevant evidence, and then in §2 I'll circle back to operationalizing exactly what I'm claiming, and finally in §3, three reasons why we should care—namely, it affects how we should think about chain-of-thought legibility, about LLM capabilities, and about LLM alignment. Note that I am not arguing that RLVR [...] ---Outline:(02:00) 1. Some relevant evidence(02:04) 1.1. Theoretically, each GPU-hour spent on RL should have orders of magnitude less contribution to LLM capabilities than a GPU-hour spent on imitative learning(03:06) 1.2. The chain-of-thought (CoT) is still obviously strongly influenced by imitative learning(04:34) 1.3. LLM companies still seem to care a lot about imitative learning (pretraining & SFT) data, not just RL environments(05:06) 1.4. Three papers claiming that non-RLVR'd models can get into the same ballpark of capabilities as RLVR'd models, although maybe we shouldn't trust those papers too much(06:56) 1.5. A paper suggesting that RLVR mostly refines the heuristics controlling which (already-known) reasoning strategy to use in which situation(09:02) 2. What am I actually claiming here?(11:30) 3. Why does any of this matter?(11:38) 3.1. Thinking about CoT legibility (both today and in the future)(15:08) 3.2. Thinking about LLM capabilities (both today and in the future)(16:33) 3.3. Thinking about LLM alignment (both today and in the future)--- First published: July 24th, 2026 Source: https://www.lesswrong.com/posts/wYpjXRLqbLbnmjbJP/llms-are-still-mostly-powered-by-imitative-learning-not-rl --- Narrated by TYPE III AUDIO. ---Images from the article:Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.

Positive reinforcement. Negative reinforcement. Positive punishment. Negative punishment.These four ABA terms are some of the most misunderstood concepts in behavior analysis, but they're much simpler than they sound.In this episode of ABA Without the Jargon, we're breaking down each term using everyday language, real classroom examples, and a few personal stories (including Skittles, chickens, and Bruno the dog

Anatol Lieven discusses the reinforcement of Ukraine following NATO meetings, highlighting German drone supplies and the symbolic value of US Patriot systems. While Baltic states fear imminent Russian provocations, Lieven argues Russia's army is currently too "bogged down" in Ukraine to risk a direct war with NATO. (1)1810 BRUSSSELS

Air quality is expected to improve today as smoke from Canadian wildfires moves out of the area.....Reports say that Midtown tower that almost collapsed may have been missing a steel reinforcement....A NYC shelter needs your help to adopt the over 900 an full 435 Fri, 17 Jul 2026 09:55:28 +0000 1whiw0dbuLu1xltQaisJdhniOfkDFkxw news 1010 WINS ALL LOCAL news Air quality is expected to improve today as smoke from Canadian wildfires moves out of the area.....Reports say that Midtown tower that almost collapsed may have been missing a steel reinforcement....A NYC shelter needs your help to adopt the over 900 an The podcast is hyper-focused on local news, issues and events in the New York City area. This podcast's purpose is to give New Yorkers New York news about their neighborhoods and shine a light on the issues happening in their backyard. 2024 © 2021 Audacy, Inc.

What to listen for:“Barking should be under stimulus control. It only happens when I cue it – or when the dog finds target odor – and nothing else.” Our hosts, Robin Greubel and Stacy Barnett, unpack the reality behind bark alerts in detection work. They challenge the idea that a barking dog is always impressive or useful, and ask the critical question: can we train a bark alert versus should we? Drawing on real-world deployment experience, they explain when bark alerts are truly required (such as FEMA USAR) and when they can create unnecessary risks, public misinterpretation, and extra maintenance for both dog and handler. They break down bark alerts as complex behavior chains involving position at source, duration, distance, and continuous barking, and show how easily “criteria creep” leads to dogs leaving odor, orienting to the handler, or only offering a token bark. Using the “dog's wallet” behavior-economics metaphor, they connect heat, terrain, fatigue, and stress to whether the dog has enough “budget” left to bark at all. The discussion also covers demand versus arousal barking, why barking must stay under tight stimulus control, and why for many teams, a solid stationary response may be a smarter, more sustainable choice than chasing a bark alert that is often easy to improperly train and hard to properly maintain. Key Topics:•       Why a Barking Dog Isn't Always Sexy (00:00)•       Bark Alert as a Complex Behavior of Duration and Distance (02:31)•       Can We Versus Should We When Choosing a Bark Alert (10:23)•       When Bark Alerts Are Required Versus When They Are Not (11:19)•       Placement of Reinforcement and The Mouse Hole Barrel Setup (14:02)•       Criteria Creep and Dogs Leaving Source or Orienting to Handler (18:36)•       Behavior Economics and The Five Dollar Dog Wallet Metaphor (31:03)•       Demand Barking, Arousal Barking, and Stimulus Control of Barking (25:30)•       Real World Limits: Heat, Fatigue, Terrain, and When Dogs Have No Gas Left to Bark (36:25)•       Why Bark Alerts Are Easy to Train Badly And Hard to Maintain Well (34:30) Resources:•       Link to training an Area Search Dog, which includes the module on a bark alert (and has a worksheet!) https://k9sensusacademy.thinkific.com/products/courses/areasearchdogWe want to hear from you:Check out the K9 Detection Collaborative FB page and comment on the episode post!K9Sensus Detection Dog Trainer AcademyK9Sensus Foundation can be found on Facebook and Instagram. We have a Trainer's Group on Facebook!Scentsabilities Nosework is also on Facebook. Here is a Facebook group you should join!You can follow us for notifications of upcoming episodes, find us at k9detectioncollaborative.com to enjoy the freebies, and tell your friends so you can keep the conversations going.And don't forget to check out the YouTube Channel!

Carl and Mike open the show with brief thoughts on the Braves limping into the All-Star break 16=5 games above .500 and with a two-game lead over the Phillies in the N.L. East. They then get into some World Cup talk as they share thoughts on the final four in which they agree, FIFA may not have been able to ask for a better set up of matchups, especially the England and Argentina game.

This week we sit down to discuss whether you should tell your dog what to do, or what not to do. We dive into the controversial reality of "positive only" dog training and discuss how trying to fix your dogs behaviour with obedience, rewards and bribes is actually making them less liberated.Plus, we sit down with Sarah and Shane to talk about their 60kg Cane Corso, Gus. After being dragged into traffic and trying every trick in the book, they share the exact shift in training that finally gave them their confidence back.

This is the 4pm All Local update for July 8th, 2026.

Interview with Dan-Mircea Mirea, MSci, author of The Reinforcement Effect of Social Media Likes in Depression. Hosted by John Torous, MD. Related Content: The Reinforcement Effect of Social Media Likes in Depression Do Likes Reinforce Depression?

Interview with Dan-Mircea Mirea, MSci, author of The Reinforcement Effect of Social Media Likes in Depression. Hosted by John Torous, MD. Related Content: The Reinforcement Effect of Social Media Likes in Depression Do Likes Reinforce Depression?

In this episode, Ray Cochrane digs into “algorithmic outing,” new research showing that social feeds can infer your sexual orientation before you have consciously come out. He also covers Meta’s privacy-aware AI infrastructure, Alberta’s 466-million-line code scan with Claude, NVIDIA on reinforcement learning, and the many journeys of learning Rust. Along the way, he hits Google DeepMind’s A24 deal, WhatsApp usernames, and scuba-diving cyborg cockroaches. Finally, he looks up with Webb’s puzzling early universe, NASA’s emergency telescope rescue, and a gorgeous aurora from orbit. – Want to start a podcast? Its easy to get started! Sign-up at Blubrry – Thinking of buying a Starlink? Use my link to support the show. Subscribe to the Newsletter. Email Ray if you want to get in touch! Like and Follow Geek News Central’s Facebook Page. Support my Show Sponsor: Best Godaddy Promo Codes Get 1Password Full Summary Cochrane opens with a quick personal update. He hopes listeners had a good holiday weekend, and he shares that he spent his time working his other job at Oregon’s Finest, chatting with people around Portland. Because his Blurbry workweek tends to be solitary, he refills his social meter on the weekends. He then recalls a Saturday night out with coworkers at the Hungry Tiger before turning to the lead story. Algorithmic Outing: When Your Feed Knows Before You Do Cochrane leads with new research from Australia that identifies a phenomenon called “algorithmic outing.” In short, the recommendation systems behind your social feeds can infer your sexual orientation or gender identity and start serving related content before you have worked it out yourself. Importantly, the study is small and qualitative, built on in-depth interviews with twenty LGBTQ+ adults in the Hunter region of New South Wales and published in the journal Gender, Place and Culture. The mechanism is engagement signals: what you like, who you follow, and how long you linger on a post, a metric the industry calls dwell time. Lead researcher Dr. Justin Ellis of the University of Newcastle notes that several participants said the algorithm “knew” they were queer before they did, an experience that felt validating for some but frightening for others in public settings. For Cochrane, the deeper worry is what else that hidden pattern encodes, from upbringing to mental health, and where that data ultimately gets sold. Sponsor: GoDaddy Economy hosting $6.99/month, WordPress hosting $12.99/month, domains $11.99. Website builder trial available. Use codes at geeknewscentral.com/godaddy to support the show. Meta’s Blueprint for Privacy-Aware AI Infrastructure Next, Cochrane turns to a sharp engineering piece from Meta on privacy-aware infrastructure. The core challenge is that a system must understand what a piece of data actually is before any privacy rule can protect it. A field named “age,” for example, might describe a person in one place and a cache setting in another. Meta’s answer deploys a large language model only on the genuinely ambiguous cases, then distills what it learns into fixed, human-reviewed rules. The payoff is concrete. According to Meta, those deterministic rules already handle about 85 percent of the traffic, and only the last 15 percent falls back to the model, which costs roughly 400 times more compute. Cochrane loves this edge-case approach. However, he contrasts it sharply with the AI-everywhere software he wrestles with at his weekend job, which he says the heavy AI reliance genuinely makes worse and harder to audit. Alberta Scans 466 Million Lines of Code With Claude This one comes from Anthropic, and it ties directly to Meta’s theme. A team inside Alberta’s Ministry of Technology and Innovation used Claude to scan 466 million lines of code in about twenty hours, a review Anthropic estimates would have taken humans roughly six and a half years. Notably, they ran around fifty AI agents in parallel, essentially an automated red team and blue team probing the systems at once. For Cochrane, this is the good version of AI in production: cleaning up and locking down real systems rather than running the show unsupervised. NVIDIA on Reinforcement Learning for AI Agents On the AI-building side, Cochrane walks through an NVIDIA developer piece on reinforcement learning for agents. Reinforcement learning rewards a model for good behavior rather than showing it the right answer, much like training a dog with treats. Additionally, he clears up a common mix-up. NVIDIA treats RAG, retrieval-augmented generation, as a separate tool: reinforcement learning changes how a model behaves, while RAG changes what facts it can reach. GitHub Retires Two Gemini Models Meanwhile, GitHub is retiring Gemini 2.5 Pro and Gemini 3 Flash across all of Copilot on July 31. The migration paths are Gemini 3.1 Pro and Gemini 3.5 Flash. Cochrane flags it as a sign of the times, since tools that felt brand new a couple of years ago are already getting sunset. He also wonders how quickly today’s “AI-optimized” chips will turn over as the models keep changing. The Many Journeys of Learning Rust One for the programmers, and Cochrane makes no secret of loving Rust. The Rust blog’s Vision Doc series explores how people actually learn the language, which is built around memory safety and its strict borrow checker. Honest themes surface throughout, including “clone guilt,” where beginners refuse to copy anything, and “silent attrition,” the learners who quietly bounce off. His take stands: getting your brain onto a memory-safe language rewires how you approach a problem. Google DeepMind Partners With A24 In an interesting collision of worlds, Google DeepMind is teaming up with A24, the studio behind Hereditary and Everything Everywhere All at Once. The two call it a first-of-its-kind research partnership, with DeepMind researchers and A24 building creative tools shaped by the artists who use them. Cochrane adds a detail worth noting: Google also invested in A24, so this is money on the table, not just a research handshake. For now, though, the announcement stays deliberately vague, with no named films or products. Google’s $1 Million Africa Indie Game Fund Another one from Google, and it is good news for developers. Google is launching an indie games fund for sub-Saharan Africa, a region whose gaming scene is growing about as fast as anywhere. The fund puts up $1 million across ten local studios, each receiving between $50,000 and $200,000 plus mentorship and hands-on support. Applications close at noon UTC on July 31. WhatsApp Usernames Are Here to Reserve WhatsApp is finally moving off phone numbers as your identity. With usernames, someone can start a conversation with you without ever seeing your number. Starting this week, you can reserve the name you want ahead of the full launch later this year. To claim yours, head into Settings, then Account, then Username. Intel Sets Its Q2 Earnings Date Cochrane flags a date worth watching for anyone tracking Intel. The company reports second-quarter results on July 23, right after market close, with an earnings call at 2 p.m. Pacific. Given recent US government investment and a shifting chip landscape, he is curious how the domestic chipmaker is holding up. Your Smartwatch Might Spot Illness Before You Do Shifting to health, Engadget reports that the wearables-plus-AI wave is starting to deliver. These devices excel at catching the moment your body drifts off its own baseline, often the first nudge to get checked out. A 2025 study from Texas A&M and Stanford suggests smartwatches can detect early signs of COVID or the flu within hours of infection. Additionally, Apple Watch’s irregular-rhythm alerts have flagged AFib correctly about 84 percent of the time. Working Memory and Consciousness Here is a heady one from Scientific American, written by philosopher Henry Taylor at the University of Birmingham. Working memory is the mental scratchpad holding whatever you are doing right now. Taylor opens with the doorway effect, that blank moment when you enter a room and forget why. Intriguingly, when information leaves working memory, it seems to leave conscious awareness at the same instant, a link drawing fresh attention across psychology, philosophy, and neuroscience. Scuba-Diving Cyborg Cockroaches Now for the wild one. Scientists have built tiny diving suits that let Madagascar hissing cockroaches survive underwater for up to three hours, while an unequipped roach suffocates in minutes. The 3D-printed suit feeds oxygen through tubes into the insect’s breathing holes, called spiracles, using a chemical generator with no electronics. This lab already steered the roaches with electrodes, so the diving suit is the new trick on top. Researchers pitch it for search and rescue, though Cochrane notes the reality of the spy bug has already arrived. Quantum Time Runs Backward at Los Alamos Next, a genuine brain-bender. Physicists at Los Alamos, led by Luis Pedro García-Pintos, found a way to make a quantum system look like it is running backward in time. To be clear, time is not literally reversing. Precise measurements just make the system’s evolution appear to unfold in reverse. The useful part is energy: measurement itself becomes a resource in what they call a continuous measurement engine. Cochrane admits the paper drifted further from his reality the more he read. Tall Trees Shrug Off Drought A new study in Science overturns some textbook wisdom. For years, the assumption held that taller trees suffer more in drought because they must lift water higher. However, researchers studying dipterocarps in Southeast Asia found that trees topping seventy meters slowed their growth by about the same amount as short ones during the 2023-2024 El Niño drought. The trick is plumbing: a seventy-meter tree grows base vessels roughly twice as wide as a ten-meter tree, so the real driver of drought stress is subtler than raw height. The Energy Department Purges Conservation Pages This next one frustrates Cochrane. The US Department of Energy deleted roughly 6,000 web pages about energy conservation, and the timing is brutal during a record heatwave. The move followed backlash over New York Mayor Zohran Mamdani urging residents to ease strain on the grid. Fortunately, the Internet Archive and its Wayback Machine preserved the pages before they vanished. For Cochrane, deleting that kind of public information simply does not make sense. Webb’s Puzzling New Universe Heading to space, Quanta Magazine explores how the James Webb Space Telescope keeps finding early-universe objects that should not exist. Those include black holes that grew enormous too fast and hundreds of mysterious “little red dots” around 650 million years after the Big Bang. As astrophysicist Rachel Somerville of the Flatiron Institute puts it, scientists have “almost gone from having too many early galaxies to having too many theories.” The hard part now is figuring out which theory is right. NASA’s Emergency Telescope Rescue NASA has a rescue mission underway for the Swift Observatory, a 2004 telescope that studies gamma-ray bursts. Recent solar storms puffed up Earth’s atmosphere, and the added drag has dragged Swift’s orbit down to about 224 miles, low enough to risk burning up this year. To intervene, NASA enlisted Katalyst Space Technologies of Flagstaff, Arizona, whose LINK spacecraft launched Friday. The plan is to boost Swift back up to roughly 373 miles. A Gorgeous Aurora From Orbit Finally, Cochrane closes on something beautiful. ESA shared a stunning aurora captured from orbit, a shimmering green band of light rippling over the planet. If you have a few minutes, it is well worth a look. Cochrane wraps with housekeeping and a thank-you to GoDaddy for two decades of support, then signs off, wishing listeners a wonderful evening. The post Algorithmic Outing: When Your Feed Knows Before You Do #1869 appeared first on Geek News Central.

Episode 072: Training and Using Stashed Reinforcement Humans understand that in order to compete, rewards have to be left outside of the ring, but does your dog understand that? In this episode, I break down how I train this concept from when my dogs are young puppies, and how I use the concept throughout their education and eventually competition. Interested in learning more? Join the conversation in my free Fans of FxAgility Community: https://www.fxagilityschool.com/signup

Stell dir vor, du gehst einmal im Jahr ins Fitnessstudio. Acht Stunden am Stück. Danach wunderst du dich, dass du null fitter bist — aber dafür einen Muskelkater hast, dass du nicht laufen kannst. Klingt bescheuert? Genau so läuft Vertriebsteam trainieren in den meisten Unternehmen. Einmal im Jahr ein Seminar. Zwei Tage Vollgas. Danach passiert: nichts. Ich weiß das aus eigener Erfahrung. Ich geh auf ein Event, schreib zehn Seiten voll, leg den Block zuhause weg — und guck nie wieder rein. Nach zwei Wochen ist nämlich alles weg. Das ist nämlich kein persönliches Versagen. Schon 1885 hat Hermann Ebbinghaus die Vergessenskurve entdeckt: Ohne Wiederholung verschwindet Gelerntes innerhalb weniger Wochen. Völlig egal, wie gut der Trainer war. Gegen Biologie kommt kein Seminar an. Trotzdem buchen Vertriebsleiter Jahr für Jahr dieses Format. Und wundern sich, dass die Zahlen nicht besser werden. Dabei weiß die Forschung längst: Vertriebsteam trainieren funktioniert nämlich nur als Daily Practice — nicht als Jahresevent. Die besten Sales-Organisationen der Welt machen das seit Jahren. Nicht weil sie schlauer sind. Sondern weil sie gemerkt haben: Tägliches Üben schlägt jedes Seminar. Und zwar immer. Wie Elite-Teams ihr Vertriebsteam trainieren — und du es kopieren kannst. Seminare sind nicht falsch. Im Gegenteil: Sie sind wichtig. Da gehst du aus der Komfortzone, lernst neue Konzepte, wirst gefordert. Aber das Problem ist nicht das Seminar. Das Problem ist, was danach passiert: nämlich nichts. Und genau hier setzt die Sales-Routine der Top-Performer an. Die Lernforschung nennt das „Distributed Practice" — verteiltes Üben. Eine aktuelle Meta-Analyse mit 242 Studien und über 169.000 Teilnehmern zeigt: Verteiltes Üben gehört zu den effektivsten Lernmethoden überhaupt. Eine Studie von Mawson & Kang aus 2025 belegt eine Effektstärke von d = 0,54. Das ist übrigens signifikant. Und zwar nicht nur im Vertrieb — in jedem Feld, von Musik bis Medizin. Die Zahlen aus der Praxis sind noch klarer. Salesforce-Daten zeigen: High-Performing-Teams coachen 2,4-mal häufiger als der Durchschnitt. Außerdem hat CSO Insights ermittelt: Verkäufer mit wöchentlichem Team-Coaching schließen 27 % mehr Deals ab. Ab drei Stunden pro Monat steigt die Quotenerreichung messbar. Und das Entscheidende: Die Gewinnerteams coachen nicht einfach mehr Stunden. Sie coachen vor allem dieselbe Fähigkeit über Wochen — bis das Verhalten automatisch sitzt. Erst dann kommt das nächste Thema. Das ist echte Sales-Routine. Die Wissenschaft hinter der Daily Practice: Warum Wiederholung alles ist. Neurowissenschaftlich passiert bei täglichem Üben etwas Entscheidendes. Bei jeder Wiederholung bildet sich nämlich Myelin um die aktiven Nervenbahnen — eine Art Isolierschicht. Dadurch werden Signale schneller und präziser weitergeleitet. Die Universität Oxford hat das messbar nachgewiesen: Wiederholtes Üben verändert die Struktur deines Gehirns. Buchstäblich. Und genau so funktioniert Gewohnheitstraining im Vertrieb. Einwandbehandlung ist kein Intelligenztest. Discovery-Fragen sind keine Talentfrage. Preisgespräche brauchen keine zwanzig Jahre Erfahrung. Das sind Skills. Und Skills werden durch Wiederholung automatisiert. Wie Fahrradfahren. Wie Zähneputzen. Du denkst nicht drüber nach — du machst es einfach. Genau das passiert, wenn du anfängst, dein Vertriebsteam trainieren zur täglichen Routine zu machen. Gong Labs hat das praktisch bestätigt. Die Analyse Tausender Verkaufsgespräche zeigt nämlich: Die besten Reps sprechen den Preis 40 % später an als der Durchschnitt. Nicht weil sie charismatischer sind. Sondern weil sie diese Fähigkeit so oft geübt haben, dass sie automatisch abläuft. Sie können sich komplett auf den Kunden konzentrieren, während ihr Gehirn die Technik im Hintergrund abspult. Dein Daily-Practice-Plan: So trainierst du dein Vertriebsteam in 15 Minuten am Tag. Jetzt wird es konkret. Du brauchst genau drei Bausteine für dein Gewohnheitstraining. Baustein 1: Solo-Einheiten — 15 Minuten am Tag. Jeder im Team bekommt einen festen Wochenplan. Montag: Einwandbehandlung. Dienstag: Discovery-Fragen. Mittwoch: Elevator Pitch. Donnerstag: Abschlusstechniken. Freitag: Storytelling. Klingt nach nichts? 15 Minuten × 5 Tage × 50 Wochen ergeben über 60 Stunden Team-Coaching im Jahr — pro Person. Plus die gemeinsamen Sessions. Zusammen sind das knapp 80 Stunden. Mehr Training, als die meisten Vertriebsteams in fünf Jahren bekommen. Baustein 2: Team-Sessions — 30 Minuten im Weekly. Dein Vertriebsmeeting hat einen festen Trainingsblock. Kein „wenn noch Zeit ist". Das ist der wichtigste Teil. Konkret machst du: Kurze Rollenspiele in Zweierteams, fünf Minuten pro Durchgang, danach gegenseitiges Feedback. Oder Best-Practice-Runden: Wer hatte den geilsten Abschluss diese Woche? Wie hast du das gemacht? Oder Fuck-Up-Runden: Wo bist du voll vor die Wand gelaufen? Alle denken mit. Genau so entsteht echte Sales-Routine im Team. Baustein 3: KI als Sparringspartner. Deine KI ist rund um die Uhr verfügbar. Kein genervter Kollege, keine Terminabsprache. Du sagst morgens: „Spiel einen skeptischen CFO aus dem Maschinenbau, der ein günstigeres Konkurrenzangebot auf dem Tisch hat." Und dann übst du. Genauer gesagt: Tools wie Yoodli analysieren zusätzlich Sprachtempo, Füllwörter und Überzeugungskraft. Das ist kein generisches Rollenspiel. Das ist messerscharfes Team-Coaching auf deine Branche zugeschnitten. Der entscheidende Trick: Verslotten — oder warum die meisten scheitern. Hier stirbt jedes Gewohnheitstraining. Nicht am Konzept — an der Umsetzung. Daily Practice funktioniert nämlich nur, wenn sie ein fester Termin ist. Feste Slots im Kalender. Mit Reminder. Auch wenn es nur 15 Minuten sind. „Das mach ich irgendwann zwischendurch" — vergiss es. Das fällt als Erstes raus. Ich hab es nämlich selbst erlebt. Monatelang wollte ich täglich eine bestimmte Übung machen. Passiert ist: nichts. Es hat erst funktioniert, als ich mir feste Slots eingetragen habe. Mit Erinnerung auf dem Handy. Bing Bong — Pitch üben. Bing Bong — Discovery-Fragen. Was nicht im Kalender steht, findet nicht statt. So einfach ist das. Noch ein Tipp: Buddy-System. Gib jedem im Team einen Trainingspartner. Einmal am Tag kurzer Check: „Hast du deine 15 Minuten gemacht?" Zusammen trainieren verdoppelt die Verbindlichkeit — wie im Fitnessstudio. Du gehst einfach eher hin, wenn jemand auf dich wartet. Warum die meisten Vertriebsleiter es trotzdem nicht machen. Es gibt ein Paradox. Ein Zwei-Tages-Seminar fühlt sich nach richtigem Training an. 15 Minuten am Tag fühlen sich nach nichts an. Nach Spielerei. Entsprechend schwer ist es, das Budget dafür zu rechtfertigen. „Wir haben doch ein Seminar gemacht" klingt gut im Jahresbericht. „Wir üben jeden Tag 15 Minuten" — naja. Aber die Zahlen lügen nicht. CSO Insights zeigt: Hochwirksame Teams setzen 4,8-mal häufiger auf Reinforcement als auf Einmalseminare. 4,8-mal! Und die neuronale Bahnung braucht allerdings Zeit: einfache Skills zwei bis vier Wochen, komplexe Verhaltensmuster zwei bis sechs Monate. Wenn es aber einmal sitzt, sitzt es. Automatisch. Ohne Nachdenken. Und zwar für immer. Das ist übrigens kein Hexenwerk. Das ist Biologie. Wenn du anfängst, dein Vertriebsteam trainieren als Daily Practice zu denken — nicht als Event, sondern als Routine — dann verändert sich alles. Nicht über Nacht. Aber Stück für Stück. Woche für Woche. Und zwar automatisch.

Show Notes/Brief Summary/Blog Post:In this insightful interview, Kim Dully discusses the importance of study skills, strategic planning, and fostering a growth mindset with Susan Ison and Bethany Wade Pinos. They explore the history of the Victus Study Skills System, the impact of technology on learning, and practical strategies for parents and educators to help students succeed in life and academics. In this inspiring conversation, experts discuss the importance of strategic planning in life, fostering independence in children, and the power of hope. They share personal stories, practical strategies, and insights on how to build confidence, develop life skills, and nurture a hopeful outlook for the future.Chapters:00:00 Introduction to the Victus Study Skills System03:13 The Evolution of Study Skills06:12 The Impact of Technology on Learning09:44 Personal Journeys and Educational Paths12:45 Strategic Planning in Education16:53 Life Skills and Future Vision19:22 Implementing Victa in Homeschooling23:49 Reinforcement and Habit Formation27:52 Overcoming Fear and Embracing Change28:57 Identifying Personal Priorities30:44 Understanding Individual Strengths and Learning Styles01:04:09 The Value of Vocational Education01:06:13 Redefining Success Beyond Traditional Education01:09:28 The Importance of Life Skills and Critical Thinking01:12:18 Empowerment Through Strategic Thinking01:14:16 The Role of Environment in Shaping Mindsets01:18:21 Encouraging Independence in Children01:22:02 Navigating Educational Resources and Support01:24:13 Success Stories and Positive Feedback01:28:32 Curriculum Structure and Age Recommendations01:31:24 Teaching Processes and Building Confidence01:37:42 The Meaning of Hope in Education01:45:10 We Have Hope Kim Outro.mp3Episode Highlights:History of the Victus Study Skills SystemImpact of AI and technology on learningTeaching life skills and strategic planningThe importance of mindset and vision for success strategic planning for personal developmentFostering independence in childrenThe importance of hope and trust in lifeRepetition as a learning toolBuilding confidence through process understandingAction Items:Reflect on your life purpose and prioritiesPractice repetition to reinforce learningEncourage children to explore their passionsUse positive feedback to build confidenceDevelop a strategic plan for personal growthMore on Victus Study Skills System:Facebook: https://www.facebook.com/VictusStudySkillsSystemSusan Ison LinkedIn: https://www.linkedin.com/in/susan-ison-71b01836/Bethany Wade Pinos LinkedIn: https://www.linkedin.com/in/bethany-wade-pinos-30517b176/Victus Study Skills System - https://studyskillssystem.org/about/HOPE Scholarship Program - https://hopescholarshipwv.gov/More on Love Your School/Links Mentioned in Episode:Visit Our Show Notes Page HERE!Questions? Email Us! kim@loveyourschool.org www.loveyourschool.orgVisit our Facebook HERE!Visit our Instagram HERE!This show has been produced by Love Your School WV.

Why does your dog lose it when strangers walk by? We break down the fear-based cycle behind stranger-directed aggression, the negative reinforcement loop that makes it worse, and the evidence-based training approach that actually works to help your dog feel safe again. Camp Lucky Board and Train City: Lee's Summit Address: 503 NW Falk Dr Website: https://campluckytraining.com

Pastor Robert Tisdale preaches a Mother's Day sermon at Tampa Life Church centered on “multiply,” teaching from Genesis 2:18 that men were created as a foundation and women were created from man's side to expand and multiply God's purpose. He explains the Hebrew phrase “ezer kenegdo,” noting “ezer” is often used of God as strong help, meaning women are divine reinforcement—equal in design and aligned in purpose—so multiplication comes through harmony, not hierarchy. Using Job 2:9–10, he warns that pain and fear can shape a home's atmosphere if unguarded, urging families to set boundaries and a tone of faith because words cultivate environments and unbelief disrupts miracles. The sermon culminates in prayer during a baptism for Jose Perez, asking for forgiveness, healing, and increased faith in the congregation.00:00 Mothers Day Pivot01:11 Multiply And Genesis02:49 Women Shape Atmosphere03:41 Foundation And Expansion05:59 Ezer Divine Strength08:55 Ezer Kenegdo Harmony10:56 Jobs Wife And Pain14:12 Set Boundaries And Tone15:38 Words Create Climate16:08 Miracles And Unbelief17:31 Baptism Prayer Moment20:43 Healing And Celebration

Start Your Transformation Now⁠  How To Have Peace of Mind In The Chaos All Around…that's huge right? I mean,it would be amazing. And, I want you to know, it's possible. I've decided to change the content format of the podcast. I want to bring a more “spiritual” approach to the podcast and I start that this week. (Bear with me as I fumble a bit trying to find my content and delivery style for this new approach.) In this episode, I talk about how to have peace of mind in the midst of what's happening in the world and I approach it from a “spiritual” aspect. In this episode I talk about:[11:47] How all the fear in the world is all “3D ego”[15:47] Reinforcement of the Be Do Have theme in the podcast and look at your BEing[21:15] How you're responding to the world[22:50] What you're learning about yourself in the world in the midst of this all[26:17] Taking advantage of the change in the world[27:32] Shamanism and letting a part of your ego “die”[29:15] Leaving your old life and routines for a new life[31:16] Letting your old “reality” die And, overall, I talk about who you have to be and what you have to do to cultivate POM (Peace of Mind) through this huge life transition we're all going through. Listen, apply, and enjoy!  As I'm shifting content and thinking about what I want to share, again, bear with me and overall this whole episode is about you leaving the old you behind you as the result of this global situation. Transformational Takeaway Without fear you have POM. Let's Connect:⁠Instagram⁠ | ⁠Facebook⁠ | ⁠YouTube⁠ | ⁠LinkedIn⁠ LIKED THE EPISODE? If you're the kind of person who likes to help others, then share this with your friends and family. If you have found value, they will too. Please leave a review on ⁠Apple Podcasts⁠ so we can reach more people. Listening on ⁠Spotify⁠? Please leave a comment below. We would love to hear from you! With gratitude, Jim

There's a moment when recalibration stops being something you do and becomes the way you move. This episode names that transition — and why the dissolution of the practice into daily life is not the end of the work. It's what the work was always for.At some point this season, the recalibration process stopped requiring conscious engagement. The recognition came before it was called for. The return from drift initiated before the drift was named. The grounded response arrived before the deliberation did.This episode is the Reinforcement stage of Week 16: Living Recalibrated. Thursday in the final week names the transition from discipline to identity — the moment the practice stops being something we do and becomes the way we move.What we name in this episode:What the transition from practice to identity actually feels like from the insideWhy the absence of effort is not the absence of the workHow the ILR pathway was always designed to internalize — not to be carriedWhat it means that the body knows the return pathway before the mind names the driftWhy the dissolution of the practice into daily life is the fullest expression of the season's purposeThis isn't about maintaining the work through ongoing discipline. Identity-Level Recalibration was designed to become the unconscious architecture of daily life — the lens, not the practice. When it does that, it stops feeling like recalibration and starts feeling like the person. That's not the end of the journey. That's the journey becoming the road.Today's Micro Recalibration: Where did recalibration happen today — without you calling it that?Explore Identity-Level Recalibration→ Schedule a conversation with Julie to see if The Recalibration is a fit for you→ Learn about The Recalibration Cohort→ Join the next Friday Recalibration Live experience → Take your listening deeper! Subscribe to The Weekly Recalibration Companion to receive reflections and extensions to each week's podcast episodes.→ Follow Julie Holly on LinkedIn for more recalibration insights→ Download the Misalignment Audit→ Subscribe to the weekly newsletter→ Books to read  (Tidy categories on Amazon- I've read/listened to each recommended title.)→  One link to all things...

The co-inventor of modern AI and the most cited living scientist believes he's figured out how to ensure AI is honest, incapable of deception, and never goes rogue. Yoshua Bengio – Turing Award Winner and founder of LawZero – is disturbed by the many unintended drives and goals present in today's AIs, their willingness to lie, and ability to tell when they're being tested. AI companies are trying to stamp out these behaviours in a 'cat-and-mouse game' that Yoshua fears they're losing.But Yoshua is optimistic: he believes the companies can win this battle decisively with a single rearrangement to how AI models are trained, and has been developing mathematical proofs to back up the claim. The core idea is that instead of training AI to predict what a human would say, or to produce responses we'd rate highly, we should train it to model what's actually true.Yoshua argues this new architecture, which he calls 'Scientist AI,' is a small enough change that we could keep almost all the techniques and data we use to train frontier AIs like Claude and ChatGPT. And that the new architecture need not cost more, could be built iteratively, and might be more capable as well as more honest.Links to learn more, video, and full transcript: https://80k.info/bengioUntil recently, the biggest practical objection to Scientist AI was simple: the world wants agents, and Scientist AI isn't one. But in new research, Yoshua has extended the design and believes the same honest predictor can be turned into a capable agent without losing its "safety guarantees."With the Scientist AI proposal on the table, Yoshua argues that it's absurd to race to get current untrustworthy AI models to design their successors, which the leading companies are attempting to do as soon as possible. But critics argue the approach wouldn't be so technically solid in practice, and that frontier capabilities are advancing so fast, and cost so much to match, that Scientist AI risks arriving too late to matter. Host Rob Wiblin and AI pioneer Yoshua Bengio cover all this and more in today's conversation.LawZero is hiring! https://80k.info/lawzero-jobsCoefficient Giving is also hiring for a range of AI-related grantmaker roles: https://80k.info/ai-grantmaker-jobsThis episode was recorded on April 16, 2026.Chapters:Yoshua Bengio on making AI honest and safe (00:00:00)The Scientist AI in plain English (00:02:26)Yoshua on how Scientist AI differs from LLMs (00:06:33)How the training data works (00:13:55)Can this become an agent? (00:20:48)Why Yoshua is more optimistic on alignment now (00:31:43)Why companies can't stop racing (00:36:05)How close to a working prototype? (00:48:27)Honest models might be more capable (00:52:40)"Reinforcement learning is evil" (01:00:28)Scientist AI from guardrail to agent (01:07:31)Can safe AI still be competent? (01:11:29)How much will this cost? (01:18:17)Can it generalise beyond maths and science? (01:22:13)A UN for superintelligence (01:37:52)Want to work with Yoshua Bengio? (01:49:32)Why smart people ignore AI risk (01:53:00)Don't let AI build the next AI (01:59:42)Why the public doesn't get the real risk (02:10:34)Why Yoshua changed his mind about AI risk (02:19:28)Video and audio editing: Dominic Armstrong, Milo McGuire, Luke Monsour, and Simon MonsourCamera operator: Jeremy ChevillotteProduction: Nick Stockton, Elizabeth Cox, and Katy Moore

What actually happens before a frontier AI model gets released — and who decides whether it is safe enough? In this episode of The MAD Podcast, Matt Turck sits down with Zico Kolter — OpenAI board member, Head of the Machine Learning Department at Carnegie Mellon, and co-founder of Gray Swan — for a deep conversation on the real risks of frontier AI. They discuss how OpenAI's safety oversight works before major model releases, why more powerful models do not automatically become safer, how jailbreaks and prompt injection expose real weaknesses in AI systems, why AI agents dramatically expand the attack surface, and where frontier AI is headed next. A clear, practical discussion on OpenAI, AI safety, AI security, AI agents, frontier models, red teaming, reinforcement learning, and the future of AI governance.(00:00) Intro(01:32) OpenAI board role and Safety & Security Committee(03:53) How OpenAI reviews major model releases(05:33) OpenAI's preparedness framework explained(09:46) Are frontier AI models getting safer?(12:33) Why AI safety does not come from scale(15:23) The four categories of AI risk(19:38) Doomerism vs accelerationism in AI(24:11) The six-month AI pause debate(26:20) AI safety as a global effort(28:04) How Zico Kolter got into machine learning(31:05) OpenAI in the early days(34:14) Why Carnegie Mellon became an AI powerhouse(38:43) What Gray Swan does in AI security(40:44) AI safety vs AI security(43:15) The GCG jailbreak paper(49:19) How AI labs responded to jailbreak research(50:19) State-of-the-art AI defenses(52:32) State-of-the-art AI attacks(54:22) Why AI agents expand the attack surface(58:39) Are AI agents ready for production?(59:40) Mechanistic interpretability explained(1:02:31) Will AI be safer in two years?(1:03:46) Reinforcement learning and self-improving models(1:08:09) Do post-transformer architectures matter?(1:09:29) Best research directions in AI now(1:11:00) Zico Kolter's Intro to Modern AI course(1:14:53) Why modern AI is simpler than people think

This video breaks down what an e-collar actually is and more importantly, how learning works when it matters most.You'll see a live self-demo so you understand exactly what the stimulation feels like, and why control, timing, and contingency matter more than the tool itself.Some dogs do very well with positive reinforcement alone.But if you've been training for months or years and your dog is still not reliable when it counts… especially with behaviors like poor recall, chasing, or high drive in real environments, then this video is for you.This is not about replacing reinforcement!It's about understanding what happens when reinforcement no longer competes with reality.What you'll see:What the e-collar really does (and what it doesn't). Why “aversive” does not mean harm. How clear, immediate, avoidable consequences create understanding. Why avoidance is not the same as living in fear. Where redirection works and where it doesn't. At some point, every training system is tested the same way:What happens when the dog is already committed?That's what this talk is about.

Alignment rarely arrives as a feeling of breakthrough. This episode names what reinforcement actually looks like — the quiet evidence of integration that shows up as absence, not presence, in the moments that used to pull you under.There's a form of evidence most high-capacity humans walk right past — not because it isn't there, but because it arrives as absence rather than presence.This episode is the Reinforcement stage of Week 15: Integration Across Life. Thursday's job has always been to name what practicing alignment looks like in ordinary life. Here in Week 15, that practice is quieter than it's ever been: the reaction that didn't come, the story that didn't build, the pull that simply wasn't as strong.What we name in this episode:Why the most honest evidence of alignment can't be tracked or loggedThe specific moment high-capacity humans mistake groundedness for going softWhy the absence of a reaction is more significant than the presence of a good oneWhat it means for a leader when the default has changed in the roomWhy reinforcement at this stage requires noticing — not performanceThis isn't a conversation about trying harder or holding it together better. When identity shifts at the root level, the nervous system updates its default. The pull weakens. The story stops building. The bracing quiets. Not because of effort in the moment — because of work that already happened.Today's Micro Recalibration: Where did something move through recently that used to settle in? Notice it. Don't grade it. Just acknowledge it as evidence.Explore Identity-Level Recalibration→ Schedule a conversation with Julie to see if The Recalibration is a fit for you→ Learn about The Recalibration Cohort→ Join the next Friday Recalibration Live experience → Take your listening deeper! Subscribe to The Weekly Recalibration Companion to receive reflections and extensions to each week's podcast episodes.→ Follow Julie Holly on LinkedIn for more recalibration insights→ Download the Misalignment Audit→ Subscribe to the weekly newsletter→ Books to read  (Tidy categories on Amazon- I've read/listened to each recommended title.)→  One link to all things...

From building Applied Intuition from YC-era autonomy tooling into a $15B physical AI company, Qasar Younis and Peter Ludwig have spent the last decade living through the full arc of autonomy: from simulation and data infrastructure for robotaxi companies, to operating systems for safety-critical machines, to deploying AI onto cars, trucks, mining equipment, construction vehicles, agriculture, defense systems, and driverless L4 trucks running in Japan today. They join us to explain why “physical AI” is not just LLMs on wheels, why the real bottleneck is no longer model intelligence but deployment onto constrained hardware, and why the future of autonomy may look less like one-off demos and more like Android for every moving machine.We discuss:* Applied Intuition's mission: building physical AI for a safer, more prosperous world, powering cars, trucks, construction and mining equipment, agriculture, defense, and other moving machines* Why physical AI is different from screen-based AI: learned systems can make mistakes in chat or coding, but safety-critical machines like driverless trucks, autonomous vehicles, and robots need much higher reliability* The evolution from autonomy tooling to a broad physical AI platform: starting with simulation and data infrastructure for robotaxi companies, then expanding into 30+ products across simulation, operating systems, autonomy, and AI models* Why tooling companies came back into fashion: Qasar on why developer tooling looked unfashionable in 2016, why Applied Intuition still bet on it, and how the AI boom made workflows and tools central again* The three core buckets of Applied Intuition's technology: simulation and RL infrastructure, true operating systems for vehicles and machines, and fundamental AI models for autonomy and world understanding* Why vehicles need a real AI operating system: real-time control, sensor streaming, latency, memory management, fail-safes, reliable updates, and why “bricking a car” is much worse than bricking an iPad* Physical machines as “phones before Android and iOS”: Peter explains why today's vehicle and machine software stack is fragmented across many operating systems, and why Applied Intuition wants to consolidate the platform layer* Coding agents inside Applied Intuition: Cursor, Claude Code, internal adoption leaderboards, and how AI tools are changing engineering workflows even in embedded systems and safety-critical software* Verification and validation for physical AI: why evals get harder as models improve, how end-to-end autonomy changes simulation requirements, and why neural simulation has to be fast and cheap enough to make RL practical* From deterministic tests to statistical safety: why autonomy validation is shifting from binary pass/fail requirements toward “how many nines” of reliability and mean time between failures* Cruise, Waymo, and public trust: Qasar and Peter discuss why autonomy failures are not just technical issues, how companies interact with regulators, and why Waymo is setting a high bar for the industry* Simulation vs. reality: why no simulator perfectly represents the real world, how sim-to-real validation works, and why real-world testing will never disappear* World models for physical AI: hydroplaning, construction equipment, visual cues, cause-and-effect learning, and where world models help versus where they are not enough* Onboard vs. offboard AI: why data-center models can be huge and slow, but onboard vehicle models need millisecond-level latency, low power, small size, and distillation-like efficiency* Why physical AI is not constrained by model intelligence alone: the hard part is deploying models onto real hardware, under safety, latency, power, cost, and reliability constraints* Legacy autonomy vs. intelligent autonomy: RTK GPS in mining and agriculture, why hand-coded path-following worked for decades, and why modern systems need perception and dynamic intelligence* Planning for physical systems: how “plan mode” applies to robotaxis, mining, defense, and multi-step physical tasks where actions change the state of the world* Why robotics demos are not production: the brittle last 1%, humanoid reliability, DARPA Grand Challenge-style prize policy, and the advanced engineering gap between research and deployment* Applied Intuition's hard-earned lessons: after nearly a decade, Peter says they can look at a robotics demo and predict the next 20 problems the company will hit* Qasar's advice to founders: constrain the commercial problem, avoid copying mature-company strategies too early, and remember that compounding technology only matters if you survive long enough to see it compound* Why 2014 YC advice may not apply in 2026: capital markets, AI company dynamics, and the difference between building in stealth with a deep network versus building as a new founder today* What Applied is hiring for: operating systems, autonomy, dev tooling, model performance, evals, safety-critical systems, hardware/software boundaries, and engineers with deep curiosity about how things workApplied Intuition:* YouTube: https://www.youtube.com/@AppliedIntuitionInc* X: https://x.com/AppliedInt* LinkedIn: https://www.linkedin.com/company/applied-intuition-incQasar Younis:* X: https://x.com/qasar* LinkedIn: https://www.linkedin.com/in/qasar/Peter Ludwig:* LinkedIn: https://www.linkedin.com/in/peterwludwig/Timestamps00:00:00 Introduction: Applied Intuition, Physical AI, and 10 Years of Building00:01:37 Physical AI vs. Screen AI: Why Safety-Critical Changes Everything00:02:51 The Origin Story: Tooling, YC, and the Scale AI Comparison00:05:41 The Three Buckets: Simulation, Operating Systems, and Autonomy Models00:11:10 Hardware, Sensors, and the LiDAR Question00:14:26 The Operating System Layer: Why Vehicles Are Like Pre-Android Phones00:19:13 Customers, Licensing, and the Better-Together Stack00:21:19 AI Coding Adoption: Cursor, Claude Code, and the Bimodal Engineer00:26:41 Verifiable Rewards, Evals, and Neural Simulation00:31:04 Statistical Validation, Regulators, and the Cruise Lesson00:40:25 World Models, Hydroplaning, and Cause-Effect Learning00:43:34 Onboard vs. Offboard: Latency, Embedded ML, and Distillation00:50:57 Plan Mode for Physical Systems and Next-Token Prediction Universally00:53:04 Productionization: The 20 Problems Every Robotics Demo Will Hit00:58:00 Founder Advice: Constraints, Compounding Tech, and Mature-Company Mimicry01:05:41 Hiring Philosophy: Hardware/Software Boundary and Engineering Mindset01:08:50 General Motors Institute, Education, and the Curiosity MindsetTranscriptIntroduction: Applied Intuition, Physical AI, and 10 Years of BuildingAlessio [00:00:00]: Hey everyone, welcome to the Latent Space Podcast. This is Alessio, founder of Kernel Labs, and I'm joined by Swyx, editor of Latent Space.Swyx [00:00:10]: And today we're very honored to have the founders of Applied Intuition, Qasar and Peter. Welcome.Qasar [00:00:17]: You guys really know how to turn it on to podcast mode. That was, you guys are real pros at this.Qasar [00:00:23]: They were just joking around right before this, and then they flipped it pretty quick.Alessio [00:00:29]: Oh, yeah, it's good to have you guys. Maybe you just wanna introduce yourself so people know the voice on the mic and they'll know what they're hearing.Peter [00:00:33]: Oh, sure. Yeah, I'm Peter Ludwig. I'm the co-founder and CTO of Applied Intuition.Qasar [00:00:38]: And my name is Qasar Younis. I am the CEO and co-founder with Peter.Alessio [00:00:42]: Nice. Can you guys give the high-level overview of what Applied Intuition is? And I was reading through some of the Congress files, when you went out there, Peter, and eighteen of the top twenty global non-Chinese automakers, you two guys, you have customers in agriculture, defense, construction. I think most people have heard of Applied Intuition tied to YC when it was first started, and then you were kinda in stealth for a long time, so maybe just give people the high-level overview of what it is today, and then we'll dive into the different pieces.Peter [00:01:10]: Yeah. So at Applied Intuition, our mission is to build physical AI for a safer, more prosperous world. And so we work on physical AI for all different types of moving systems, everything from cars to trucks to construction and mining equipment, to defense technologies. And we're a true technology company, so we build and sell the technology, and we sell it to the companies that make the machines. We sell it to the government, really anyone that wants to buy a technology to make machines smart.Physical AI vs. Screen AI: Why Safety-Critical Changes EverythingQasar [00:01:38]: Yeah. And I think in the broader AI landscape, a lot of the focus, rightfully so in the last, three years has been on large language models, and so everything fits in a screen. Like, whether it's code complete products or things like that. And what's different about us is we're deploying intelligence onto a lot of things that don't have screens. they're physical machines. There are sometimes screens within the cabin or for example of a car or a truck or something like that, but most of the value we provide is putting intelligence that is in safety critical environments. So that those two words are really important because learn systems can make mistakes if you're asking for, like, some, so something like, “Tell me about these podcast hostsQasar [00:02:28]: that I'm about to go meet.” But you can't do that obviously when you run, like, as an example, we run driverless trucks in Japan right now, as we speak. We can't have errors. Those are L4 trucks. Yeah.Alessio [00:02:40]: Yeah. Was that always the mission? I remember initially, I think people put you and Scale AI very similarly for some things about being kinda like on the data infrastructure side of things. What was the evolution of the company?The Origin Story: Tooling, YC, and the Scale AI ComparisonPeter [00:02:51]: Well, from the very beginning, we always wanted to, really be a technology company that helped generally push forward the industrial sector. And so we started off working in autonomy. Our very first customers were robotaxi companies. And we started off doing a lot of work in simulation and data infrastructure. And then over the years, we've expanded our portfolios. Now we have, over thirty products, and it's a pretty broad technology play within the landscape of physical AI.Qasar [00:03:19]: Yeah, I think the Scale reason is because we're all YC Universe companies. But it was a very different company. Scale, was, is more of a services company, data labeling company fundamentally. We started and still are, do a lot of tooling. So like, you think developer tooling is now in vogue again, thanks to the AI boom. But honestly, ten years ago, it was out of vogue. It w Like, doing a tooling company in 2016, 2017 was not, like, the thing to do because, I don't know if you remember, the VCs generally, their views was that toolings are They're just workflows, and workflows ultimately are not really interesting. And we've gone and come, full circle with that. But when we started the company, our kind of it's kinda like in the periphery of what the company wants to be. It was like, from our earliest days, like, we wanna deploy software on physical machines, like on cars and on trucks and things like that. And obviously, we didn't know that the transformer boom was gonna happen. We didn't know that autonomy systems would become end-to-end. Those things we didn't know. And why that's important when autonomy systems become end-to-end, it is just now those models can be generalized to, multiple form factors. And so back nine, ten years ago, tooling was a great way, and still is a great way to, build the technology and sell technology to our end customers, a lot of them who wanna build this stuff themselves. And so we just offer like a spectrum of solutions from you can just use like one part of a development suite of tools all the way to buying the full thing. The way to think about the company, or at least the way we think about the company is, as Peter said, a technology provider. It's kinda like, what NVIDIA does or what an AMD, but we just don't do chips.Qasar [00:05:06]: We don't do silicon. But we're a technology provider fundamentally. And I think even, we used to joke when we started the company, like, we're not the guys to build, like, Instagram. Like that was just towards That's not our That's just not us in a most fundamental way. IAlessio [00:05:20]: You have thoughts.Qasar [00:05:21]: Yes.Qasar [00:05:22]: Well, it's, it's I mean, I think it's just like what And I mean, we worked on Maps and stuff, Google Maps. Consumer products are extremely difficult for a lot of different reasons. It just, I think doesn't scratch the itch. I think we're like Michigan guys who are kind of more of that traditional engineering kind of a realm, or lineage. we used to jokeThe Three Buckets: Simulation, Operating Systems, and Autonomy ModelsPeter [00:05:41]: I gotta say, though, what was clear ten years ago was that there was so much more that was possible with software and AI in vehiclesPeter [00:05:47]: and that was generally the space that we started in ten years ago.Peter [00:05:51]: And the precise path that we've taken over the years, I think we've been strategic, and we've adjusted to make sure that we're actually building stuff that's valuable to the market. And like, the technology has changed so much. Like our own technology stack has completely changed, I would say, roughly every two years. And so now we've probably done, let's say, four complete evolutions of our own technology stack. And I sort of see that cadence roughly keeping up.Peter [00:06:13]: And so the way even we think about engineering is almost on this two-year horizon, we're preparing ourselves that, hey, like, we wanna invest the appropriate amount, but then also be very dynamic as the research gets published and as our research team figures out new advancements and adapting to that.Qasar [00:06:27]: Yeah. One thing that has been consistent is the type of people we've, we've recruited. It's engineers who are fall into the sometimes very traditional, like, GoogleQasar [00:06:38]: -gen suite, but way different from, other companies. We are hiring folks who really know the intersection of hardware and software, who know really low-level systems. Obviously, traditional ML researchers and folks who've, actually, put ML systems into production. That's been pretty consistent. I think that, like, you look at the mix of our engineering, eighty-three percent of the company is engineering, so it's, like, a giant list.Qasar [00:07:05]: A lot of engineers.Alessio [00:07:06]: Which, by the way, a thousand engineersQasar [00:07:07]: Yeah. A thousand engineers.Alessio [00:07:08]: that's on your website, so I imagine it's up to date.Qasar [00:07:11]: It is, it is up to date, yes. Yes.Alessio [00:07:12]: okay. And then forty-plus founders.Qasar [00:07:15]: Yeah. We would tend to also, This was more luck than strategy. But we've recruited a lot of ex-founders. It's been a great place for founders, YC and non, ‘cause obviously I know a lot of the YC folks. It's kind of like we recruit a lot of Google people.Qasar [00:07:33]: For them to exercise both their technical and non-technical skills because, we're, we're, we're on the applied side. We have a research team that we do fundamental research, we publish, and we've, we've had great traction there. But fundamentally, the business wants to take this intelligence and deploy it into production and there's, like, a certain type of person that's more interested in that.Alessio [00:07:54]: Yeah. You mentioned the tech stack, Peter, so I just wanted to give you some rein to just go into it. I'm interested in where Wayve Nutrition, starts and ends in some sense, what won't you do? What, do you do that's common among all the verticals that you cover?Peter [00:08:10]: There's a few buckets of work that we do, and we've been at this for almost ten years now, so the technology's pretty broad. But we got startedQasar [00:08:17]: Yeah, with a thousand engineers, like, you could work on lots of things.Peter [00:08:19]: There's lots of stuff, yeah, espe-especially with AI tools to help.Peter [00:08:22]: So we got our start in simulation and simulation tooling and infrastructure. And so generally, if you're trying to build a very complex software system that involves moving machines, you need to test that, and the best way to test it is it's a combination of virtual developments, a simulation, and then also obviously real world testing.Peter [00:08:39]: And then there's a very careful process of that correlation between the simulation results and the real world results and ensuring that the simulator is in fact accurate to that. Simulation's a very deep topic.Peter [00:08:49]: We have a whole suite of products in that, and we could talk for many hours about that specifically. But that is one part of what we do as a company. Reinforcement learning as a subpart of that is also super critical. I think a lot of the a lot of the best advancements happening in a lot of these AI systems right now in some way relate to reinforcement learning, and with now we have lots of compute, and you can do tons of interesting things for reinforcement learning. The second bucket of work that we do is on operating systems technology. true operating systems. Like, think about, schedulers and memory management and middleware and message passing and highly reliable networking and data links. Like, the reality is, if you want to deploy AI onto vehicles, you need a really good operating system. And when we were getting deeper into that space, there wasn't really anything that we were happy with.Peter [00:09:39]: Like, things existed, absolutely, and we were using what was available in the market, and as an engineering organization, we roughly realized these things aren't great. We think we can do this better, and so let's, let's build something. And that was then the that was the moment of inspiration that started our operating systems business, which is now a very real business for us. And in order to write and run great AI, you need a great operating system, and so that-that's what got us into that. And then the third bucket that we work on, it's, it's true fundamental AI technology. Models, we do a lot of work in, as mentioned, the foundational research, but then the also the world models and the actual autonomy models that are running on these physical machines, and that's across cars, trucks, mining, construction, agriculture, and defense, and so that's both land, air, and sea.Qasar [00:10:31]: And also, a smaller subsector of that third bucket is the interaction of humans with those machines.Qasar [00:10:38]: So that's a multimodal, experience. Historically, if you're moving a dirt mover or any of these machines, there are, like, buttons you press, whether they're actual physical tactile buttons or something like a touch screen. That's just That fundamentally is changing to where you're just talking to the machine and the machine and you're teaming with the machine.Alessio [00:10:58]: Voice?Qasar [00:10:59]: Yeah, voice, absolutely, yeah.Alessio [00:11:00]: Oh.Qasar [00:11:00]: And also the machine just being aware of who is in the cabin, what their state is. you can think from a safety systems perspective, the most simple version of this is, like, the driver is tired, right? They're, they're if you get those alerts when you're driving your car and saysHardware, Sensors, and the LiDAR QuestionQasar [00:11:15]: -maybe take a coffee break, that take that times, a couple of order of magnitudes up. But this concept of teaming man and machine is important. When you think about running agents or just running, different instances of, Claude and doing work for you in the background, you can take that analogy out, almost copy and paste and put it into, like, a farm, where you have a farmer who's running a number of machines. So where they interact with the machine is where there's maybe a critical decision or a disengagement or something like that, but generally speaking, the agent on the physical machine is running and making decisions on the behalf of the farmer until there's something maybe critical. And that's also what we work on. So that's not pure autonomy. It's a little bit of a mix, but it falls under, autonomy. In the automotive sense, that's typically defined in SAE levels as an L2++ systemQasar [00:12:05]: -with a human in the loop. But just take that idea, to other verticals.Alessio [00:12:09]: Yeah. You've not mentioned hardware at all, like sensors or obviously we you mentioned you don't do chips. I think even in AV there's, like, a big, cameras versus lidars. Like, what are, like, in your space maybe some of those design decisions that you made, and are they driven by the OEM's ability to put things on the machinery? And like, how much influence do you guys have on co-designing those?Peter [00:12:32]: Yeah. So we don't make sensors. Like, we're, we're not a manufacturer. Obviously, we use a lot of sensors in our autonomy products. in terms of what actually goes on the vehicles, we have a preferred set of sensors that we, let's say fully support, and then our customers, they can sort of choose from those. And obviously if there's a very strong opinion on supporting something else, we'll add that to the platform as well. And the lidar question is at this point sort of the age-old,Peter [00:12:59]: topic in autonomy, and the state of the industry right now is lidar is hands down a useful sensor, specifically for data collection and the R&D phase of autonomy development. if you see, for example, a Tesla R&D vehicle, it actually has lidar on itPeter [00:13:17]: to this day, right? In the Bay Area we see these. you'll see, like, Model Ys or Cybercab that have lidars on them just driving around. So it's, it's useful because it gives you per pixel depth information. So if you can pair a lidar with a camerand you can say that, well, this camera's looking this direction, this lidar's looking this direction, and now for each pixel of the camera I can see how far away is that pixel. you can actually then use that as a part of your model training, and then the that depth information then becomes a learned, a learned state of the camera data. And then when you're doing the production system, you can now remove the lidarPeter [00:13:52]: and now you can actually get depth with just the camera. And so that difference between, like, a highly sensored R&D vehicle and then the down-costed production vehicle, we use that across our whole portfolio of products. And of course the end goal is you want super low cost and super reliable.Peter [00:14:08]: And then in certain use cases you have some more, bespoke things. Like in defense as an example, you do things at night oftentimes, and so you care about sensors like infrared, more so than And you don't, you don't wanna be putting energy out, so you don't wanna use lidar or radar.Peter [00:14:23]: but you still need to be able to see at nighttime. So yeah, we work the whole gamut.The Operating System Layer: Why Vehicles Are Like Pre-Android PhonesAlessio [00:14:27]: Cool. So that's kinda like on the hardware level. Then on the OS level, how does that look like? What is, like, unique? my drive- I drive a Tesla. Whenever I drive some other car that has a screen, it always sucks.Alessio [00:14:38]: It's on, like, cheap Android tablet. It's like, it's laggy and all of that. What does the OS of, like, the autonomy future look like?Peter [00:14:46]: When most people, it's really what you just described. When you think about operating system in a vehicle, you're thinking about the HMI, right? The human machine interface, and absolutely that's a an important part of it, but that's actually only one thin layer on top. So when we talk about operating systems for, like, AI in vehicles, there's many layers that go deep into the CPU critical realm and embedded systems, and you're talking about the real time control ofPeter [00:15:13]: let's say the electric motors or the engine and the actuators, and you have different redundancies for different, let's say, the steering actuation in the vehicle. And all of these things, need very core support in the in the operating system. And then of course for autonomy you have real time sensor data that's streaming in, and the latencies there are really important, right? If you try to Imagine you try to run Microsoft WindowsPeter [00:15:35]: like streaming your sensor data in or controlling the vehicle. Like, the latencies are gonna be absurd. Like, you can never do that. And so what's special about what we do is we really have this system level thinking, right? So we're looking at, we care about every performance characteristics of the entire system, and then we also, because we're doing a lot of the software or all of that software, we can fine-tune and control all of those things. So we can very carefully tune in the latencies for every aspect of the system. We can carefully tune in the memory management. We can have the right, fail-safes and fallbacks, for different things. ‘Cause you have to account for what if, what if there is a critical failure? What if there's a cosmic ray that flipsPeter [00:16:14]: a bit in the middle of the processor that causes some, malfunction? And you have to have a fail-safe to all of that, and so the core operating system is a part of that. And then the one last thing, which is a lot less exciting but is, actually a very big topic, is reliability of updates.Peter [00:16:30]: so the I have a Tesla and you get updates fairly frequently, right?Peter [00:16:36]: Once a month. Most companies that are making vehiclesPeter [00:16:40]: are basically never doing updates, and they're And even if they are doing updates, they're usually only updating maybe one module. Maybe they're updating the HMI module. But they're not able to update, let's say, the CPU critical parts of the system.Peter [00:16:51]: You have to go into the dealer for that. And so with our operating system now we can actually enable highly reliable updates of any system in the vehicle, and that's way easier said than done. Like, there's lots of technical, technically deep stuff, in the tech stack to do that in a way that you're not going to accidentally brick a vehicle.Peter [00:17:08]: And right? If, imagine yourAlessio [00:17:10]: That would be bad.Alessio [00:17:11]: Bad.Peter [00:17:11]: Bricking a car is a very expensivePeter [00:17:13]: and honestly, like across the industry maybe one of the most just pure impactful things that we've done is we've just, we're, we're now enabling the industry to actually do software updates.Alessio [00:17:22]: Just to clarify as well, who is the customer for this? Like, I assume a lot of hardware manufacturers have their own firmware, and I'm sure some of them would just have you write it for them because you're experts. And others would have their own. Like, who pays for this? Who invites you into the house? Is it, is it the end user, or is it, is it the manufacturer?Peter [00:17:41]: Yeah. So let me make an analogy firstly on the on the fragmentation of software. So physical machines today are more akin to the state of the phone market before Android and iOS existed, right? So I worked on Android at Google by the way many years ago, and part of the reason that Larry at Google decided to get into Android was they wanted to run Google products on a bunch of phones, and they bought all of these phones from the industry, and it turned out they had like 50 different operating systems on these phones. And it was virtually impossiblePeter [00:18:17]: for Google to make their app run on all 50 devices equally well. And so the solution was, well, actually what if, what if they created-A really great operating system and made it attractive to all of these phone makers, and that was sort of the genesis for what Android was and why Android existed. It was a way for Google to get their products onto really wide diversity of devices. The state of the physical, industry right now, it's a little bit like that. Like, there's yes, these companies have firmware, but they have so many different operating systems, it's so fragmented, and to actually get a modern AI application to run on these vehicles, you actually, you first have to consolidate the operating system, and so that's, that's why we've done that. And then, your specific question was who are our customers? It's, it's, generally it's the companies that are making these machines.Peter [00:19:06]: And we're, we're, we're selling our technology to them to really simplify the architecture and then enable these AI applications to run on them.Customers, Licensing, and the Better-Together StackSwyx [00:19:13]: How much is reusable across? Like, do you have, like, one OS that is just configured for everything, or is there some more customization that is needed?Peter [00:19:22]: Yeah, highly reusable. So the fundamental technology is quite universal, right? So things that we do have to think about though are, like, chipset support. And so if you're, if you're coding, let's say, an LLM and you have start with an assumption that, “Hey, oh, I'm gonna, I'm gonna use CUDA, and I'm gonna run this, on an NVIDIA chip,” then you don't really have to think about the hardware in that sense. Like, you're just, “Okay, I'm just I'm in the CUDA/NVIDIA ecosystem, and I'm, I'm going to use that.” But the hardware, especially in safety critical systems, it's a lot more diverse. There's not one or one or two players. There's a bunch of different chipsets that we have to support. And so our operating system doesn't just run on, like, the equivalent of X86. It has to, it has to run on a number of different architectures from chips from a bunch of different companies. But again, we've been working on this for a long time now, so we have, we have support for all of those chipsets. And then when you want to then run the AI applications, we can then do that reliably across now a variety of providers.Qasar [00:20:19]: And I think that is, like, heavily inspired by Android, right? Android has a huge suite of testing and it's a reliable operating system that runs on thousands of devices. And we think we can, we can do the same in all these physical moving machines, with the difference that we're really in a safety critical realm. Android isn't.Alessio [00:20:40]: So on Android, I don't need to use Gmail, I can use Superhuman. Like, what about your machinery? Like, can people bring somebody else's automation to it, or is it kinda like all-in-one?Qasar [00:20:50]: You have to use us. No. Yeah. we're If, Yeah. Yeah, it's totally open. Yeah.Peter [00:20:56]: Yeah. our philosophy is that we are a technology company, and so we license our technology to customers to use how they want. And so if a customer wants to If they wanna license our autonomy tech and our operating system, then great, we'll license those. If they just wanna license the operating system and then use different autonomy tech, that's fine also, and we have great documentation andSwyx [00:21:17]: Or if they wanna use developer tooling.Peter [00:21:18]: Yeah, exactly.AI Coding Adoption: Cursor, Claude Code, and the Bimodal EngineerSwyx [00:21:19]: It's, like, a better together if, obviously, if you, if they work together. Is it all C++ I assume is with different compile targets?Peter [00:21:27]: We use a lot of C++.Peter [00:21:28]: Rust is sort of a hot, the new hot kid on the blockPeter [00:21:32]: for a bunch of things as well. But yeah, the lower level you get, especially when you get to real-time constraints, you hit C++ at some point, and at some point maybe you work your way into assembly when needed.Swyx [00:21:44]: Oh, damn.Alessio [00:21:46]: I'm curious about the coding agent adoption, just, like, since you're mentioning more esoteric languages. Like, what's the adoption internally? What have you learned?Peter [00:21:55]: Yeah. We use everything. So Cursor was, I think the hottest tool in the company for a good while. Now Claude Code, I think has taken the reign on that. We have a internal leader, leaderboard that we use just to sort of encourage adoptionPeter [00:22:09]: with-within the company. And yeah, it's, they're phenomenally useful. it's, Honestly, we take inspiration from some of those tools also in how we're adapting some of that mindset of thinking to the physical realm. Like if it's so easy to build an app for this or that thing that lives just on a screen, we can We're taking now a lot of the same ideas and applying that to, “Okay, well, if you wanted a physical machine to do something, how easy can we make that, using our own tooling and platform as well?”Alessio [00:22:40]: Are you changing any of, like, the OS architecture, kinda like the way you expose services to, like, be more AI friendly or?Peter [00:22:48]: Yeah, absolutely. The in the early days of our tools infrastructure work, it was a lot about, You had engineers that were experts in certain topics, but the things that you're dealing with, they're oftentimes more mathematical or more abstract, where actually GUI tools are very useful for certain things. Like as an example, we have a product we call Sensor Studio, which is, it helps you design the sensor suite for your autonomous vehicle, whether, again, it could be a car, it could be a drone, could be a mining equipment, could be a robot. And you place sensors in different places. You There's different, There's a library. You can understand what are the trade-offs that you're making in the design of that system, and that was, like, a very, a very GUI intensive, thing ‘cause it's a little more like a CAD tool in that senseSwyx [00:23:37]: YepPeter [00:23:37]: if you've seen CAD tools. Nowadays, though, right, we expose all of the underlying APIs for that and now using, AI agents, you can actually configure a sensor suite with just text and likely reach a better result than you could've through the GUI in the past, and we're taking that thinking now through the whole product portfolio.Swyx [00:23:57]: Another thing I was thinking about is just in terms of, like, AI, adoption, does it change your hiring at least a little bit, or how do you, how do you sort of manage engineers, differently?Peter [00:24:08]: Yeah. absolutely, it does. we, I think like every company in the Valley right now, are evolving our hiring practicesPeter [00:24:16]: because the skills required to be effective are changing so fast, right? you used to really select for just rote implementation ability and now it is more the AI engineer skill set, right? Where it's like, yeah, how to implement, but actually-Just banging out code is no longer the core job, right? It's, it's actually knowing what questions to ask, knowing how to tie, how to tie together these different AI tools. And so the interviews that we give now I think are way harder than they've ever been.Peter [00:24:46]: But we also allow, right, selective use of AI tools to solve the problems. And I think in that you start to see more of a bimodal distribution of engineers, right? You start to see like wow, there's, there's this subset of people that they really get it. Like they're, they're all in and they've, they've clearly invested the hours needed to learn these tools and how to be effective.Peter [00:25:09]: And then there's sort of the group of people that haven't done that, and that the productivity gap is just enormous. And so we're, we're trying to obviously select for the people that are really into this.Qasar [00:25:20]: I first wrote the my AI engineer piece three years ago, and when I first wrote about it, I was like, “Actually, not everyone should be an AI engineer,” ‘cause I think there's a there's an extremist stance where well, every software is an engineer is an AI engineer. And my actual example of people who should not be adopting AI was embedded systems and operating systems, and database people. Are they adopting AI?Peter [00:25:41]: I think it's the classic bitter lesson, topic, which is the Six months ago I would've said the same thing, but it's, it's becoming super useful for every domain.Qasar [00:25:53]: I'm sure.Peter [00:25:54]: Right? Like,Peter [00:25:56]: there was, I think six months ago, or maybe a year ago, if you tried to use, let's say the latest Claude model for writing shaders, GPU shaders, the results were probably underwhelming. And if you use the latest model now to do that kind of task, you're a little bit blown away, like, “Wow, that actually worked. That's amazing.” And we see the same thing in the embedded realm. No question though, especially when you get into safety critical systems, the human validation isPeter [00:26:25]: is 100% key. Like I You're not gonna trust your life to a an AI written software that's, that's not been very carefully, checked by humans. And so I think now the really the challenge is about that appropriate level of human validation for these safety critical systems.Verifiable Rewards, Evals, and Neural SimulationAlessio [00:26:41]: How do you think about, yeah, touching on the simulation side, I think verifiable reward and reinforcement learning is, like, the hottest thing. What have you done internally to build around that? And like, what gives you What makes you sleep at night? Like, if somebody's like, just web coding something or likeAlessio [00:26:57]: wants to try something new, you have like a good enough system. Because I think the opposite is also true, is like if it's super easy to write anythingAlessio [00:27:04]: then it puts a lot of work on like the verifiableAlessio [00:27:07]: side of it. Like, what does that look like for people?Peter [00:27:10]: Yeah. So verifiability, a broader bucket of like evaluations, right? Like how do you evaluate the results that you're, you're getting? I think this is probably the hardest problem right now, because the As the models get better, it can be harder and harder to find the faults on the system.Peter [00:27:29]: And so like the problem of doing proper eval to find those faults, like that problem also keeps getting harder as the models get better. But it's no less important than it's ever been, right? You still there are still going to be edge cases that are not met and whatnot. And so it's, it's a big area of investment for us. On the reinforcement learning topic, the key thing is there's all these new requirements that come to be in the latest generation of these technologies. So for example, end-to-end is the big thing right now in autonomy and physical AI, which is you can now train these models that can effectively take sensor data in and then put control signals out, and get really good results out of that. But the way that you train and improve those models is really different from the previous generations. And so to do reinforcement learning on an end-to-end model, you now need to actually simulate all the sensor data, right? So then this becomes a we call our, work in this neural simulation, but it'sPeter [00:28:26]: think of it like a hybrid of Gaussian, splatting and diffusion methods, and where you really care about performance. Like performance is everything. If you can't do enough simulation fast enough and cheap enough, you actually can't get results that are worthwhile, in the end. It also gets to a lot of our work in embedded systems, which is like performance critical work, and that performance optimization, performance criticality, it carries over to a lot of the model training work. because, like, the only way to make it affordable is it has to be really fast.Qasar [00:28:58]: I think it's worth a few minutes talking about our own, evolving thoughts on verification and validation withinQasar [00:29:05]: kind of, traditional simulators, which are, you can think of like vehicle dynamics or something like that, which you're just taking textbooks and taking those formulasQasar [00:29:13]: and putting them into software, to like now this neural sim/world model universe. I think that's an interesting topic.Peter [00:29:20]: Yeah. So in more traditional development, right, you oftentimes would have, more black-and-white answers to questions.Peter [00:29:28]: And so the in Europe as an example, there's, a regulatory, system, it's called Euro NCAP. It's the European New Car Assessment Program, and as part of that, the vehicles have to pass a bunch of tests, and those tests actually, include, safety systems. So automatic emergency braking for a child that runs in front of a carPeter [00:29:51]: or let's say an occluded child that runs out and you hit it. And so you have You end up with sort of these binary answers of like, well, did the car under test pass this specific test? And there's a very well-known set of test casesPeter [00:30:05]: that the vehicle has to pass. And that was how the industry worked, let's say, until 10-ish years ago. But what's changed now is with these models, everything is statistics, right? Like you no longer have a black-and-white answer, but it's like, well, how many orders of magnitude or how many nines of reliability can I get in the system, and how can I, how can I prove that to be true? And the big unlock honestly for physical AI as an industry is that these models are just becoming much more reliable. Right? Things like things actually work a lot better. It's like the number of nines you can get out of these systems are now good enough that it actually becomes cost effective to really deploy these things. And so the big shift in, so verification and validation has been from a little bit more of a Again the past it was strictly requirements, and are you meeting or not? And now it's more of a statistical, verification and validation case where it's all about how many nines of reliability and meantime between failures, that sort of thing.Statistical Validation, Regulators, and the Cruise LessonSwyx [00:31:04]: And is the target audience regulators or even the customers are yeah, if you I imagine the customers are bought in, and it's mostly regulators that need to be satisfied.Peter [00:31:15]: We do work with the US government, we do work of course with the European governments and the government of Japan, and the government is not like an AI lab by any means.Peter [00:31:25]: So Swyx [00:31:26]: They just care about the outcome.Peter [00:31:27]: They care about the outcome.Peter [00:31:28]: And so we do education, in that regard, and like so sort of teaching about, “Hey, this is how we think validation should be done, and this is an approach that we think is reasonable,” and how to think about like when is a driverless system actually safe enough to go on the roads and that sort of thing. But I wouldn't say that the government is asking for it. It's like we're more teaching the government in that, in that sense. It's honestly, it's more so for our own, our own comfort, right? Like, we want to build very safe systems, and then of course our customers care deeply about that as well. But in that context we're also typically educating our customers.Qasar [00:32:01]: Yeah. Our first, our first core value is on round safety. So I think we can't underline enough that, us also verifying and validating that the systems that we're deploying are safe to us is probably as important as, like, some regulator or a customer saying,Swyx [00:32:19]: Of course. Okay. Yeah.Swyx [00:32:20]: You have to satisfy yourselves.Peter [00:32:22]: As I say, as a whole across the world, regulation oftentimes it's like a almost lowest common denominator. But like, you really have to substantially exceed what the regulators are expecting to make good products.Swyx [00:32:33]: Yeah. One thing I often talk about, I think and I try to make this relatable to the audience also, is Cruise, where they had an accident that basically ended the company. I wonder if people overreact to single incidents, because incidents are going to happen regardless, right? ‘Cause it's a statistical thing, but as long I don't know if regulators understand that, you cannot extrapolate from a single incident, but we do because that's all we have to go on. And your sample sizes are necessarily gonna be lower than, I don't knowSwyx [00:33:00]: consumer driving.Qasar [00:33:01]: Yeah. I think the Cruise example wasn't a technology failure. there was The real, compounding issue there was just how did the company talk to the regulators and what was their kind of behavior, and I think that became more of the issue. If you look,Peter [00:33:19]: It isn't It definitely was a technology failure, but it was made much worse by theSwyx [00:33:23]: Put the car back on the woman.Qasar [00:33:25]: Yeah. And let me put it another way. There is a version where Cruise still exists.Swyx [00:33:29]: right. Right.Qasar [00:33:30]: Right. It'sSwyx [00:33:30]: It was like the last strawQasar [00:33:31]: ItSwyx [00:33:31]: in like a long chain ofSwyx [00:33:33]: like issues.Qasar [00:33:33]: So do you feel like ATG had that horrific accident or someone actually dying, because, that was a homeless person crossing the street? So yeah, I think we can't understate enough that ultimately, like, statistical validation of something, that's one part of it, but it's not the only part of it. Like, consumer and let's say, mainstream adoption of these technologies is also gonna be part of that conversation. I think companies like Waymo are doing a lot of service positively to the industry in the sense of they're, they're setting a high benchmark and they're showing, kind of in a very responsible way how to, how to deal with these. There have been Waymo incidences as well. They've just not been as significant as the Cruise one that you mentioned. But yeah, so I think you'll just continue to see that. I think probably the long term question is really gonna be, again, around Like it is very clear humans are way worse drivers statistically.Qasar [00:34:29]: Like, there's no, there's no debate. And so at what point But we're emotional animals.Swyx [00:34:34]: Yeah. So my thing is, like, we have to get to a point as a society where we accept horrific accidents that would never happen by a human because statistically we understand that it is safer overall. In the same way that planes, they're safer, than I think they're the safest mode of transport that we have.Qasar [00:34:50]: Yeah. it's more dangerous to drive to the airport than it is to get on a flight.Qasar [00:34:53]: So if you're everQasar [00:34:54]: if you're ever getting nervous about getting on a plane, just think “I just gotta get to the airport.”Swyx [00:34:58]: Yes, we're flying.Qasar [00:34:59]: If I get to the airportQasar [00:35:00]: I'll be good.Swyx [00:35:00]: But then it's, planes also concentrate the tail risk if planesQasar [00:35:03]: Yeah. AndPeter [00:35:04]: And I was, I don't think we honestly have to worry about there ever being, accidents from these systems that are like much worse than what humans would cause, ‘cause humans do terrible things.Peter [00:35:14]: Like, people fall asleep at the wheel all the time.Swyx [00:35:16]: I have.Swyx [00:35:17]: Like, I'll call, I've been a drowsy driver.Peter [00:35:19]: Kinda drunk drivers, and that'sPeter [00:35:20]: that's the extreme end of the example. But these AI systems, you have redundancies, you have fallbacks. Like, there's many things have to go wrong for there to actually be a something catastrophic because there's, there's so many, fallbacks that these systems have.Alessio [00:35:36]: your simulation is like so vast because there's so many use cases. What are, like, maybe things that worked in a simulation and then you put it out and it's like, “F**k, this isAlessio [00:35:45]: this just did not work at all?”Peter [00:35:47]: Yes.Alessio [00:35:47]: IsPeter [00:35:47]: That's maybe a bit of a misconception, about simulation there. So let me go a little bit, more technical on this. So at first go, no simulation is going to represent the real world. There's always a process of this, sim to real matchingPeter [00:36:02]: where you actually, you need the real world feedback to basically feed into the parameters that are being used in the simulator, and you have to do that, it's like this validation flow, a number of times until you can get some confidence that, like I think the simulator is now accurately representingPeter [00:36:19]: what's gonna happen in the real world. Now, if you have a situation where you've done that full validation and you thought that it was accurate and then there's something different, those are much trickier cases, and that's, that absolutely can happen, but really I think the validation process is a really important part. You can never skip the simulation validation process, like where you're actually ensuring that, hey, the actual, my sim to real gap here is small enough that I can trust these simulation results. And there's, there's so many fun things that you can do when you get into it. Like, I'll, I'll give one fun example that came up recently is like in these humanoid robotics, systemsOverheating actuators is a real problem, right? So obviously phenomenal demos. IPeter [00:37:01]: The most amazingAlessio [00:37:02]: For 10 minutes.Peter [00:37:03]: The most amazing I can get. I love, I love watching robots do acrobatics like everybody but the these systems actually overheat, right? If, like, And one of the ways you can use simulation though is you can actually have that, the temperature of those actuators be one of the parameters that's representedPeter [00:37:18]: in the simulation. And if you're doing reinforcement learning over a certain task, then the robot can actually adjust its motions in the simulation to account for the fact that, oh, it knows that as it's moving, it's actually beginning to overheat this motor. But if you didn't have that parameter of, let's say, the heat of that motor represented in the simulation initially, then your RL policy might It will disregard that. And now you run that on the robot and the robot will overheat and fail.Alessio [00:37:43]: I guess the question is, like, how do you have all of these parameters taken care of while also understanding the deployment environment? Like, temperature is like a great example, right? WellAlessio [00:37:53]: why did you make my robot worse when it runs in like a freezer?Alessio [00:37:57]: So it actually shouldn't worry about that. it's like, yeah, how do you design these simulations?Peter [00:38:02]: This is honestly the This is what makes simulation so hard, right? it's because you Simulation is fundamentally about you're trying to optimize the development of a system, right? Like, how can I build this system faster and better and cheaper and what are all the levers that I have to actually accomplish that? And because simulation's just a software program, you can, you can change it a lot more easily than you can hardware systems. And then what's particularly awesome about the let's say, world models and using that as a part of simulation is now the simulation doesn't just scale with, let's say, adding new math equations inPeter [00:38:36]: but we can actually scale the simulation environment now with additional real world data and that also unlocks a whole new field of robotics.Qasar [00:38:46]: There is a meniscus line where you cross where still doing real world testing is better. there's, in this, sim-to-real gap, you can reproduce reality at exceedingly expensive costs and this So nothing is free. So really you have to you're finding that line where you're getting great performance, you're getting great feedback, whether it's on the training side or on the eval side, but it's way cheaper than doing it in the real world. At some point it, that doesn't make sense. And so even, from our earliest days in autonomy, our view was you're still gonna do real world testing. You There's, there's not, there's not this, magical land where you're not gonna do that. And maybe even like a more nuanced version of this in like traditional software development is, most of your testing for software in a vehicle, 95% of that can be like traditional CI/CD kind of, flows that you would have in traditional web development. But once you have Now you, let's say you have a truck. Well, you can do like 4% of those in like a rig which has all the components, the electrical and electronics of a truck, but doesn't have, it doesn't have the tires and it doesn't have the And then you have the 1%, which is actually the vehicle. There's something There's a similar analogy in terms of using simulation for intelligent systems. You can do a lot in a simulator, but in using world models, but ultimately it's, it's physical AI. So you're gonna deploy it on physical machines andQasar [00:40:17]: the freezer example comes to, comes to light.Alessio [00:40:20]: The world model thing has been to me the hardest thing toAlessio [00:40:22]: wrap my head around. Like we have Faith Eliyon on the podcast.World Models, Hydroplaning, and Cause-Effect LearningQasar [00:40:25]: We've been doing a small series with like another Intuition company, General Intuition as well.Qasar [00:40:31]: yeah, and I mean, lots of, lots of coverage on NeRFs and yes.Alessio [00:40:34]: Yeah. It feels like we talk with about, the heliocentric system, right? It's like in a world model, if you just feed visual data, the model might learn that the sun spins around the Earth. It makes sense, right? And it's like, well, not really. And I think what are like some of these other things that like hydroplaning is one thing I think about, is like can a world model understand hydroplaning and like what amount of water like causes it to happen? And it's like, yeah, to me it's like I don't understand how you guys do it. I guess it's like the real thing is like when you're doing both cars and the highway in Japan versus the excavator in a mine in,Qasar [00:41:13]: ArizonaAlessio [00:41:13]: wherever you're Arizona, wherever you're deploying them.Alessio [00:41:15]: How much of it are you relying on the world models to like generate the simulations for you and then try and close the gap after versus like giving the world models as a tool to your engineers to like curate the simulations if that makes sense?Peter [00:41:28]: Yeah, totally. So yeah, I can say at a pure engineering level, I think if you're hoping to do real world deploys and you're purely relying on a world model approach, you probably won't get to something that works, before you go bankrupt. So there is just a very practical mindset of like, world models are amazing and they're extremely useful for a lot of use cases, but there are a lot of other things that you need to do to actually get something started and something deployed and working. most fundamentally, world models are all about It's understanding the world, but also understanding what's going to happen. It's like the cause-effect relationship.Peter [00:42:01]: Right? And so like it, right, if you have a take some sort of construction tool, and that construction tool is gonna be doing some work on the Earth in some way, it's gonna be moving earth, the world model needs to understand that cause-effect relationship. Like, okay, when I, when I take this material from here and put it over there and now I have things that are over here and not over there anymore and that cause-effect, relationship. data obviously is a is a big problem. The hydroplaningPeter [00:42:26]: one is actually a really great example because it's actually quite non-obvious sometimes. Right? It's like, well, it's, it's raining and well this road, has, let's say the appropriate curvature to it so the water is running off the road and cars are driving faster here and then you approach a road that's very flat and water is now puddling on that road and all of a sudden cars are driving slower because when they were driving faster they were starting to lose control. And there are a lot of visual nuance, very nuanced visual cues in the scene and so I do think in the world model concept there's a good chance that the model actually would learn that you should just drive slower when these visual cues exist, and that's obviously the beautiful-The beauty of, these kinds of models where they just, they learn these non-obvious things.Swyx [00:43:14]: It doesn't need to know about hydroplaning to know that it needs to drive slower.Peter [00:43:17]: Yes.Swyx [00:43:17]: I guess it's Yeah. I wanna ask questions about, also deploying models. I presume, like, you use a lot of these world models for training data and simulation, but what about deploying it onto the systems in production? Presumably you have you have, like, GPUs on deviceOnboard vs. Offboard: Latency, Embedded ML, and DistillationSwyx [00:43:36]: but they're I keep saying on device. What's the what's the right term for that?Peter [00:43:40]: On machine.Swyx [00:43:41]: On machine.Peter [00:43:41]: Or embedded, yeah.Swyx [00:43:42]: Yeah. What is the embedded world like? because for people who are not used to that world, this is very alien.Peter [00:43:49]: Yeah. So it's actually We call it onboard and off board.Peter [00:43:52]: So like, onboard software and off board software.Peter [00:43:54]: And the great thing about off board software is you don't have to care about time, and you can run really large models, right? So you can, you can say, “Well, this model, I don't care if it takes one second for it to give me a result or 10 seconds for it to give me a result, because we have time.” And the models can be really big, and they can run, in a data center or on a on a huge GPU and you can obviously have distribute to compute, et cetera. But onboard you don't have any of those benefits. You're like, “Well, I need I have this many milliseconds where I need an answer from this model.” And so a lot more of the energy then is about, think of it more like distillation and it's like truly efficiency and like, literally every fraction of a millisecond counts. And you can't have a situation where the model takes too long because then the vehicle can't actually function.Peter [00:44:42]: And so you can, you can still use a lot of the same techniques, and the models themselves you can think of as like a derivative of larger models that you can run offline, and then you're, you're trying to just get a model that is still performs really well but it's, it's a it's smaller, small enough version that you can then run on this embedded system where you care about latency and power.Qasar [00:45:03]: Yeah. And I think like, the broader point I think which, maybe is not obvious but it's worth saying is in physical AI world, we're not really constrained right now by, like, the intelligence of the models. It's actually what Peter's talking about, it's actually deploying them inSwyx [00:45:19]: The hardware they give you.Qasar [00:45:21]: Yeah. On the hardware you give you.Qasar [00:45:22]: And so And there's just a reality is of safety critical systems. So those end up being the your limiting factorsQasar [00:45:29]: rather than, let's say, a limiting factor for, a foundation model companyQasar [00:45:34]: is gonna be just capital maybe or researchers.Qasar [00:45:38]: So we're, we're in that way dealing with, for us as people who kind of come in that realm with like a very interesting Those constraints force creativity.Swyx [00:45:47]: And I imagine, nobody was deploying or giving you the hardware for transformers back in 2018, whatever, but now they are. What's the evolution like? just peel back the curtains a little bit.Peter [00:45:59]: Yeah. Transformers first off, I think the paper was originally published in 2017.Swyx [00:46:02]: 2017.Swyx [00:46:02]: So there's no time.Peter [00:46:04]: And ISwyx [00:46:05]: But I'm just saying I guess I'm saying, like, embedded ML systems usually, like, a lot less parameters, a lot less compute, and now, like, orders of magnitude more.Peter [00:46:14]: Yeah. absolutely. what I was gonna say though was I think in the in the original paper in 2017, maybe it's in the last paragraph, somewhere in the paper they talk about, like, “Oh, by the way, this technique might be useful for, like, images and videos as well.”Peter [00:46:30]: These last subjects.Peter [00:46:31]: And it took a few years for that impact to really hit. But like, now, we're seeing transformers are everywhere.Swyx [00:46:39]: Yeah. Vision transformers.Peter [00:46:40]: And then then the compute just keeps getting better and better. But you do have this fundamental trade-off, right? It's like you have power, you have cost, and performance and like, getting the right, getting the right mix of those things in an embedded package that can also be, like, shaken and baked in all thePeter [00:47:00]: conditions that these things have to have to operate in. But yeah, I think that they're only going to keep getting better and so we also try to plan our strategy understanding that, we know the rate of improvements of these systems.Swyx [00:47:11]: Yeah. So like, Google just released the Gemma 2B modelSwyx [00:47:15]: that effective 2B model. Is that useful to you guys or is that too big?Peter [00:47:18]: You can run that model on an embedded system, definitely.Peter [00:47:21]: the So yes, it's, it's useful in that regard. The bigger question is, like, what do you use it for in an embedded system? Like, you actually need to customize it quite a bit to make it useful for something. But yeah, you could run a two billion parameter model, definitely.Swyx [00:47:35]: It also interesting, like, what percent is a custom ML model that only does that thing versus a generalist LLMSwyx [00:47:41]: which probably is not that useful actually for your context.Peter [00:47:46]: Like, you, like, you can imagine different use cases, right?Peter [00:47:48]: So theSwyx [00:47:49]: The voice stuff, yes.Peter [00:47:49]: Yeah, the voice test. Totally, yes.Peter [00:47:51]: So for the actual, autonomy elements, that's 100% in-house. We do every bit of that, the data simulation, the model, everything. But when you get into the more generic use cases like voice or voice assistant kind of thing, that's where these more generalist models like Gemma actually can be quite, can be quite useful.Swyx [00:48:09]: Yeah. And then there's also obviously a trade-off between, like, what percent must you do on machine, versus just call home.Peter [00:48:16]: Yeah. It's all about latency.Swyx [00:48:17]: Latency.Peter [00:48:17]: It's all about latency. Yeah.Swyx [00:48:18]: Yeah. Well, like, I think actually in a lot of contexts, especially in the US, you can just have a connection to the web.Qasar [00:48:26]: Yeah. I think though most of our universe is everything has to be fairly, embedded and local because just the nature of Even in the US there's a lot of likeSwyx [00:48:39]: PatchinessQasar [00:48:40]: don't haveQasar [00:48:41]: have coverage, right? And if you look at, like, the old world of autonomy within mining, which is, like, long before transformers and kind of, neural networks, in the like CNN and kind of a universe, they were really just hand-coded, systems. They were just like, this machine is gonna run to that place with thisPeter [00:49:03]: That was our GPS, like very accurate GPS.Qasar [00:49:05]: Yeah. And so that worked, and that worked for 20 years, so why would we actually need to use transformers or kind of more modern end-to-end systems? Mainly because you can only really run a path and run backwards. That provided a lot of value, but m-Not as much as you get when the machine is actually intelligent. It's, it's seeing, it's perceiving, it's acting in a dynamic world.Alessio [00:49:28]: I looked up RTK, real-time kinematic, one to two-centimeter accuracy.Qasar [00:49:32]: Yeah. Fantastic. But the and fantastic in faraway lands where there's not gonna be cell phone coverage.Peter [00:49:39]: Yeah, so it's widely used on the legacy mining and agricultural autonomy systems today. So like, for example, a combine that can be precise within one or two centimeters as it's driving down the field, they use RTK.Qasar [00:49:53]: Yes.Peter [00:49:53]: But it's, it's expensive.Qasar [00:49:54]: Yeah. And it's, it's, it's autonomy, but it's not intelligent in the way that I think all of usQasar [00:49:58]: if in twenty-six we'd be talking about intelligence.Alessio [00:50:00]: In one of your blog posts, you mentioned research on large scale transformers that are similar to those doing modern generative AI. What are, like, the big differences other than, “You're absolutely right. I should steer the car, so you probably wanna remove that?”Peter [00:50:14]: We have a diversified bet strategy internally, and the reason we've done that is because we operate in now a bunch of industries, a bunch of geographies, and each of the approaches has, obviously a different risk to them.Peter [00:50:27]: And so like, we're not going to put all of our eggs in a single basket for a single approach because that approach may no

The armor kept us safe in the seasons we needed it. But armor doesn't distinguish between threat and love. And it's been keeping the people closest to us at a distance we never intended.Most of us didn't lose trust in others all at once. It happened in accumulation — the relationships that didn't hold, the vulnerability that got used against us, the closeness we allowed that left us more exposed than we intended.And somewhere in the aftermath, we made a quiet decision. We put on armor.We didn't call it armor. We called it wisdom. Healthy boundaries. Discernment about who earns access. And some of that was genuinely right.But here's what armor doesn't know how to do: distinguish.It keeps the people who would harm us at a distance. And it keeps the people who love us at exactly the same distance.This is the Reinforcement stage of Week 14 — and today the week's work lands in the hardest place: relationship. Because trust doesn't stay interior. It shows up in whether we're present or managed. In whether the people closest to us can reach us — or whether they're pressing against armor they can feel but simply cannot name.There's an important difference between discernment and armor. Discernment is about who earns access. Armor is about denying access to everyone — including the people who've already earned it.We get to keep our discernment. We get to be thoughtful about who receives the real version of us. But when the armor stays on with people who've proven they're trustworthy — when they're getting the managed version instead of the real one — that isn't wisdom anymore. That's the protection that has outlived its purpose.And the cost isn't just ours. It belongs to every person on the other side who has been trying to love us and keeps finding the managed version instead.Is this episode for us?We show up to relationship but aren't quite reachableThe people closest to us are getting the capable version, not the real oneArmor and discernment have started to look the same from the insideToday's Recalibration:Think of the person who has most consistently shown up for us. Are they getting the real version — or the managed one?Explore Identity-Level Recalibration→ Schedule a conversation with Julie to see if The Recalibration is a fit for you→ Learn about The Recalibration Cohort→ Join the next Friday Recalibration Live experience → Take your listening deeper! Subscribe to The Weekly Recalibration Companion to receive reflections and extensions to each week's podcast episodes.→ Follow Julie Holly on LinkedIn for more recalibration insights→ Download the Misalignment Audit→ Subscribe to the weekly newsletter→ Books to read  (Tidy categories on Amazon- I've read/listened to each recommended title.)→  One link to all things...

If our behavior plans only kick in after things fall apart, we are already too late. We explore how strong classroom management starts with prevention, not reaction, and how the structure of the environment shapes student behavior. From clear expectations to smooth transitions, we unpack what actually makes group settings run effectively.We reflect on how small proactive strategies, like priming, visuals, and teaching routines, can completely shift classroom dynamics. We also discuss why inconsistent reinforcement, unclear roles, and long wait times often lead to challenging behavior, and what to do instead.Throughout the conversation, we emphasize that good classroom management is simply good teaching. When we build systems that support all learners, we reduce the need for reactive strategies and create more positive, engaging environments.We also share practical ways to teach expectations, reinforce success, and create meaningful motivation so that students are set up to succeed from the start.What's Inside: Why prevention is more effective than reactionHow structure, routines, and transitions impact behaviorSimple strategies to improve reinforcement and engagementMentioned in This Episode:Episode 067: How To Use ABA in ClassroomsReinforcement Systems Starter PackHowToABA.com/joinHow to ABA on YouTubeFind us on FacebookFollow us on Instagram

You've repaired… but why does the same cycle keep coming back? In this episode, we explore the third step of The Repair Practice: Reinforce—the often-missed piece that helps your energy, boundaries, and decisions actually last. After we recover, it's easy to slip back into old patterns. Reinforcement is what helps you break that cycle—not through force or discipline, but through support, discernment, and aligned choices. We explore: Why repair alone isn't enough The patterns that quietly lead to repeated wear What reinforcement actually looks like in daily life How to trust your gut and protect your energy The difference between force vs support Why reinforcement can be gentle, not rigid If you've ever felt like you're doing the work… but still ending up in the same place, this episode will shift how you approach lasting change.  

This episode explores the concept of ownership and partnership through both equine science and human relationship psychology, examining how power, dependency, and learning shape the horse-human relationship.Sources & Further ReadingsEquine Behavior & WelfareHausberger, M., Roche, H., Henry, S., & Visser, E. K. (2008).A review of the human–horse relationship. Applied Animal Behaviour Science, 109(1), 1–24.https://doi.org/10.1016/j.applanim.2007.04.015 Sankey, C., Richard-Yris, M. A., Henry, S., Fureix, C., Nassur, F., & Hausberger, M. (2010). Reinforcement as a mediator of the perception of humans by horses. Animal Cognition, 13(5), 753–764.https://doi.org/10.1007/s10071-010-0326-9 Fureix, C., & Meagher, R. K. (2015).What can inactivity (in horses) tell us about welfare? Applied Animal Behaviour Science, 171, 8–20.https://doi.org/10.1016/j.applanim.2015.08.016 Stress & Physiological IndicatorsVisser, E. K., et al. (2002).Heart rate and heart rate variability during a novel object test and handling in young horses. Physiology & Behavior, 76(2), 289–296. Schmidt, A., et al. (2010).Cortisol release, heart rate, and heart rate variability in horses. Hormones and Behavior, 57(3), 319–325. Learning Theory & TrainingMcLean, A. N., & McGreevy, P. D. (2010).Ethology and learning theory in horse training. In Equitation Science. McGreevy, P., & McLean, A. (2007).Roles of learning theory in equitation. Journal of Veterinary Behavior, 2(4), 108–118. Human Relationship PsychologyDeci, E. L., & Ryan, R. M. (2000).Self-determination theory and the facilitation of intrinsic motivation. American Psychologist, 55(1), 68–78.(Discusses autonomy, competence, and relatedness in relationships) Mikulincer, M., & Shaver, P. R. (2007).Attachment in Adulthood: Structure, Dynamics, and Change.(Explores security, responsiveness, and relational safety)

Welcome to the Strength Connection!Huggy McNiff is Performance & Nutrition Coach with Trevor Kashey Nutrition, and one of the most impactful people I've ever worked with in my life.In this episode, Huggy shares insights on behavior change, mastery, and sustainable success in fitness and life. Discover how deep knowledge, precise language, and effective reinforcement strategies can transform your approach to health and personal growth.Check out more from Huggy at:IG: https://www.instagram.com/coach_huggybear5326?igsh=MWJzaTI3emRpeDIyMA%3D%3D&utm_source=qr50 % off TKN Summer Shred program:https://go.trevorkasheynutrition.com/kickstart-your-summer---podcastChapters00:00 Introduction and Personal Impact05:26 The Journey of Coaching and Personal Growth09:53 Behavior Modification and Coaching Principles16:27 The Role of Integrity in Mental Toughness20:38 Approach to Client Success and Long-Term Change21:41 Behavior Change Through Observation24:19 The Role of Coaches in Modeling Behavior25:16 Misconceptions in Nutrition: The 80-20 Rule28:15 The Importance of Precision in Nutrition29:41 Integration vs. Balance in Life31:51 Intuitive Eating: A Skill to Master36:39 Efficacy vs. Confidence in Coaching37:59 Developing Autonomy in Clients43:09 The Importance of Social Support

If you've ever walked into a hard conversation already braced for impact — this episode is about what happens in the sixty seconds before. Presence in conflict isn't about staying calm. It's about who is in the driver's seat.Most people prepare for conflict by preparing their words. They run through scenarios. They anticipate responses. They build a case. And then the conversation begins — and the nervous system, which has been on alert since the preparation started, takes over before the identity can get there.Staying present in conflict is not about staying calm. Calm is a feeling. Presence is a practice. You can be fully activated — heart rate elevated, body clearly aware that this conversation matters — and still be present. What presence requires is not the absence of activation. It requires that identity, rather than threat response, is in the driver's seat. And getting identity into the driver's seat is a somatic practice before it is a verbal one. It starts in the body, before the words, before the room.This episode is the Reinforcement stage of Week 12 on conflict. Reinforcement here means practicing a new way of being inside a hard conversation — not through technique or script, but through the intentional, pre-conversation regulation that allows identity to lead rather than threat response to drive.In this episode you'll recognize:Why staying present in conflict is not the same as staying calm — and why that distinction changes everything about what you're trying to doHow anticipation of conflict activates the nervous system before the conversation even begins — and what that costsThe pre-conversation practice of prayer, breath, and conscious body relaxation — and why sixty seconds before the call changes what happens inside itWhy presence is a somatic practice before it is a verbal oneWhat it means to still be in the practice — not as failure, but as faithfulnessToday's Micro Recalibration:Before your next hard conversation, take sixty seconds. Pray or orient — remember who you are before the room can tell you otherwise. Breathe intentionally, signaling to your nervous system that you are not under threat. And consciously relax your body — find where you are holding and release the bracing before the conversation begins.Explore Identity-Level Recalibration→ Schedule a conversation with Julie to see if The Recalibration is a fit for you→ Learn about The Recalibration Cohort→ Join the next Friday Recalibration Live experience → Take your listening deeper! Subscribe to The Weekly Recalibration Companion to receive reflections and extensions to each week's podcast episodes.→ Follow Julie Holly on LinkedIn for more recalibration insights→ Download the Misalignment Audit→ Subscribe to the weekly newsletter→ Books to read  (Tidy categories on Amazon- I've read/listened to each recommended title.)→  One link to all things...

Does your classroom ever feel like controlled chaos? In this episode, we unpack what's really behind busy, overwhelming ABA classrooms and how we can better support both students and staff. We explore why behavior plans alone often fall short and how strong systems can make all the difference when things get loud and unpredictable.We walk through practical, proactive strategies like building flexible routines, organizing the physical environment, and using visual supports to increase independence and reduce stress. We also dive into common breakdown points like transitions and share ways to teach and reinforce key skills before challenges escalate.Beyond student support, we focus on the critical role of staff. From clear expectations to communication and emotional regulation, we highlight how empowered, supported teams are essential for success. Ultimately, we remind ourselves that classrooms don't need to be perfect, just functional, supportive, and sustainable.What's Inside:How to prevent chaos with simple, proactive systemsStrategies for smoother transitions and skill-buildingSupporting staff to create calm, effective classroomsMentioned in This Episode:Episode 127: Classroom ReinforcementManaging the Mayhem: Supporting Busy Classrooms and Group Settings HowToABA.com/joinHow to ABA on YouTubeFind us on FacebookFollow us on Instagram

We've been taught that dog training comes down to four quadrants—reinforce what you like, punish what you don't. Clean. Simple. Effective… right?Not quite.In this episode, we're taking a step back and looking at what actually drives behavior: the nervous system. Because before a dog can learn from consequences, they have to be in a state where learning is even possible.If your dog is stressed, overwhelmed, or living in a constant state of survival, it doesn't matter how “correctly” you apply the quadrants. Reinforcement won't land the way you think it will. Punishment may suppress behavior, but it won't resolve what's underneath it. And what looks like disobedience is often a dog doing the only thing their nervous system knows how to do to stay safe.We'll break down the four quadrants in simple terms, then walk through what happens when you try to apply them to a dysregulated dog. More importantly, we'll talk about what needs to come first—safety, regulation, and an understanding of the dog in front of you.Because training doesn't start with behavior.It starts with state.And until we shift that, we're not modifying behavior—we're just managing symptoms.dogspeak101.comdogspeakgeek.thinkific.compatreon.com/dogspeak

In this episode of The Ross Simmonds Show, Ross sits down with Britney Muller, AI educator and founder of Orange Labs, to unpack what marketers are getting wrong about large language models, why reverse engineering ChatGPT is a dead end, and how to build real leverage in a probabilistic world. From practical AI workflows to the ethical risks shaping the future of the industry, this is a first-principles breakdown of what actually matters next. Key Takeaways and Insights: 1. AI is not search, it is a different machine entirely - LLMs are probabilistic word prediction systems, not ranking engines. There are no ranking factors inside ChatGPT and no URLs in its training data. - Most marketers are forcing AI into an outdated SEO mental model, and new technology requires a new framework. 2. Understanding RAG and how visibility actually works - LLMs are often paired with real-time search to stay current, but the core model and the retrieval layer are two separate systems. - Visibility in AI requires influence across both training data and search ecosystems, and SEO still matters even as the mechanics are shifting. 3. Brand mentions over backlinks - LLMs magnify what appears most frequently in training data, which means contextual brand mentions are becoming leverage. - One startup paid for brand mentions on commonly retrieved URLs rather than links and it worked. Distribution across relevant conversations increases the probability of surfacing. 4. Why you cannot reverse engineer LLMs - There is no deterministic ranking system to hack. Outputs vary across identical prompts because of probabilistic modeling. - Most AI tracking tools rely on synthetic prompts and crude metrics. Guarantees in GEO are dangerous and honesty builds trust. 5. Build your own AI tracking stack - Internal tools are now cheaper and more powerful than off-the-shelf platforms. Running prompts multiple times per day allows teams to measure probability ranges. - APIs allow thousands of queries at minimal cost. Control your data and do not outsource your intelligence. 6. Real AI workflows built by marketers - Competitive engagement scraping combined with AI-personalized outreach is producing 80 percent response rates. HARO filtering systems can now auto-draft responses inside Slack in real time. - The common thread across every workflow that works is the same: start with a clear problem, then layer in AI. 7. AI as personal leverage - Brittany used ChatGPT to win a home bidding war with a personalized letter and reframed a payment dispute email as a lawyer, which resulted in payment within 30 minutes. - AI is not just marketing leverage. It is life leverage. Literacy creates power. 8. Is SEO dead? Not quite. - Google patents suggest AI-first interfaces may replace traditional SERPs, and organic traffic levels will likely not return to pre-AI highs. - The pie may shrink but search will not disappear. Off-site distribution and social proof will matter more than ever. 9. The ethical risks of AI power - A small group of decision-makers controls foundational AI systems, and the incentives in place favor hype cycles and growth over accountability. - Reinforcement learning optimizes for pleasing users, not truth. AI literacy must include understanding bias and power structures. 10. The rise of AI agents - Early agents were mostly hype, but new iterations like Claude Chrome integrations can now visually interpret and act inside browsers using screenshot-based reasoning. -The future of marketing may involve AI transacting on behalf of users entirely, and execution changes workflows. Resources & Tools:

In this episode, we explore the powerful technique of marker training and why timing plays a critical role in your puppy's success. You'll learn how to clearly communicate with your dog using marker words or a clicker, how to reinforce the exact behaviors you want, and how to avoid common timing mistakes that can slow progress. Whether you're just starting or looking to sharpen your training skills, this episode will give you practical tools to build better habits, improve focus, and strengthen your bond with your puppy.Support the showFollow us on social mediaInstagram @BAXTERandBella Facebook @TheOnlinePuppySchool YouTube @BAXTERandBellaSubscribe to our site for FREE weekly training tips! Check out our FREE resources!Join our membership here.

Setting boundaries in relationships can create quiet relational strain and fear of losing connection. This episode explores why boundaries feel risky, not because you're harsh, but because identity and belonging have been intertwined — and how recalibration restores alignment.Can you set boundaries without losing people?For many capable, high-responsibility adults, the real fear behind boundaries is not conflict.It's distance.Less warmth.Less access.Less relevance.In this Reinforcement episode of The Recalibration, we explore the identity-level tension beneath relational boundaries — especially for those who learned early that being needed secured belonging.When usefulness becomes identity, clarity feels dangerous.You're not afraid they'll explode.You're afraid they'll quietly adjust.You're afraid of becoming less necessary.Less central.Less indispensable.This episode gently names what often goes unspoken:The fear that alignment will cost you attachment.Through the lens of relationships, attachment, and nervous system regulation, we examine why boundaries are not just behavioral shifts — they are identity shifts.When we stop over-explaining, people feel it.When we stop rescuing tension, dynamics change.When we stop being the emotional thermostat, the room recalibrates.And that shift can feel like loss before it feels like depth.This is where Identity-Level Recalibration (ILR) is distinct.ILR is not a communication technique.Not a productivity tool.Not boundary scripts.It is the root-level recalibration that makes every relational behavior sustainable. Because identity precedes behavior.This episode supports:– Relationship strain without visible conflict– Identity misalignment beneath burnout– Fear of losing relevance in leadership relationships– Emotional exhaustion from over-functioning– Attachment anxiety in high-performing adultsToday's Micro Recalibration:In one conversation this week, experiment with saying one sentence less than usual.Don't clarify it.Don't justify it.Let it stand.Notice what rises in you.Not to judge it.Just to observe it.Reinforcement is how new identity becomes embodied.Explore Identity-Level Recalibration→ Schedule a conversation with Julie to see if The Recalibration is a fit for you→ Learn about The Recalibration Cohort→ Join the next Friday Recalibration Live experience → Take your listening deeper! Subscribe to The Weekly Recalibration Companion to receive reflections and extensions to each week's podcast episodes.→ Follow Julie Holly on LinkedIn for more recalibration insights→ Download the Misalignment Audit→ Subscribe to the weekly newsletter→ Books to read  (Tidy categories on Amazon- I've read/listened to each recommended title.)→  One link to all things...

Video: https://www.youtube.com/watch?v=5lsQIJUPgQ4&t=15sPart 1: https://youtu.be/uKa3wzpRoxQ?si=57tk2tO14VNVdzcpIn this episode, you can learn:Why the brain repeats rewarding behaviors and avoids costly onesHow dopamine and norepinephrine shape motivation, effort, persistence, and quittingWhy habits and routines emerge as energy-saving strategiesHow autistic cognition can heighten attention to detail, discrepancy detection, and internal weightingWhy the brain is always trying to maximize expected value while minimizing metabolic costSee the show notes from episode 1 of the Internal Calculators and Motivation for previous links.@daylightcomputerco‬ Daylight Computer Company, use "autism" for $50 off at https://buy.daylightcomputer.com/autismand Daylight Kids (!!!) https://kids.daylightcomputer.com/autism ‪@getchroma‬ Chroma Light Devices, use "autism" for 10% discount at https://getchroma.co/?ref=autism0:00 Internal Calculation Review: Reward, Cost, Value, Control & Habit Formation3:01 Uncertainty, Control, the ACC & Why Habits Reduce Effort5:40 Autism, Sensory Precision & Detecting Small Discrepancies6:36 Dopamine, Reinforcement & the Biology of Motivation11:57 Norepinephrine, Attention, Effort & Cognitive Engagement15:17 Astrocytes, Persistence, Quitting & Effort vs Outcome17:12 Reward Hijacking: Addiction, Smartphones, Social Media & Repetition20:33 The Equation of Life: Expected Value – Metabolic Cost22:39 Stable vs Chaotic States: Which Brain Networks Dominate24:38 Deep Focus, Flow, Habits & Why the Brain Automates Responses26:39 Final Takeaway: Maximize Value, Minimize Uncertainty & Conserve EnergyX: https://x.com/rps47586YT: / @fromthespectrum@Rfsafe https://rfsafe.org/mel/podcasts.php?pick=source%3Afromthespectrumemail: info.fromthespectrum@gmail.com

In the Best of the Bears this week, Tribune reporter Brad Biggs joined the Mully & Haugh Show to share his takeaways from general manager Ryan Poles' press conference Thursday and to discuss the need for Chicago to improve its pass rush; Matt Spiegel and Laurence Holmes discussed the Bears' need to bolster their defensive line; and Leila Rahimi and Marshall Harris took calls from Score listeners who shared their thoughts on whether the Bears should pursue Raiders star defensive end Maxx Crosby in a trade.

In the Best of the Bears this week, Tribune reporter Brad Biggs joined the Mully & Haugh Show to share his takeaways from general manager Ryan Poles' press conference Thursday and to discuss the need for Chicago to improve its pass rush; Matt Spiegel and Laurence Holmes discussed the Bears' need to bolster their defensive line; and Leila Rahimi and Marshall Harris took calls from Score listeners who shared their thoughts on whether the Bears should pursue Raiders star defensive end Maxx Crosby in a trade.

In the Best of the Bears this week, Tribune reporter Brad Biggs joined the Mully & Haugh Show to share his takeaways from general manager Ryan Poles' press conference Thursday and to discuss the need for Chicago to improve its pass rush; Matt Spiegel and Laurence Holmes discussed the Bears' need to bolster their defensive line; and Leila Rahimi and Marshall Harris took calls from Score listeners who shared their thoughts on whether the Bears should pursue Raiders star defensive end Maxx Crosby in a trade.

Financial alignment can still carry pressure, especially when your authority feels tied to control. This episode explores why exhaustion around money isn't a discipline issue, but an identity-level misalignment—and what steadiness actually feels like in your body and leadership.What does financial alignment actually feel like?Not in a spreadsheet.Not in a net worth milestone.But in your nervous system.Many high performers carry quiet financial pressure—even when the numbers are strong. There's still a subtle tightening. A readiness. A need to stay ahead.This isn't about irresponsibility.It isn't about greed.And it isn't about lacking discipline.It's about identity.When financial steadiness becomes fused with authority, credibility, and safety, control can start to feel virtuous. Being the most disciplined person in the room becomes a form of security. And loosening that grip can feel like losing your edge—or even losing yourself.In this Reinforcement stage of The Recalibration pathway, we explore what alignment actually feels like in your body:• The difference between control and stewardship• Why financial vigilance often feels safer than relationships• How identity load ties competence to belonging• The quiet grief of releasing superiority as safety• Why steadiness sharpens leadership instead of dulling itThis episode weaves nervous system regulation, identity shift, and leadership relationships together. Because burnout around money is rarely about math. It's about misalignment.Financial alignment does not mean shrinking your ambition.It means building without bracing.For those who carry responsibility for others—teams, investors, family—this episode gently asks:Can I remain ambitious without being dominant?Can I lead without using money to stabilize my identity?Can I stay steady without tightening?Today's Micro Recalibration:Think of one real financial decision you're navigating right now. As you picture it, notice your body. Do you brace? Speed up? Mentally rehearse proving your competence? Now ask gently: What would steadiness feel like here?If you lead others, notice this too: When you talk about money, does the room feel safe—or activated? What would 5 percent more calm look like this week?Explore Identity-Level Recalibration → Schedule a conversation with Julie to see if The Recalibration is a fit for you → Learn about The Recalibration Cohort→ Join the next Friday Recalibration Live experience → Take your listening deeper! Subscribe to The Weekly Recalibration Companion to receive reflections and extensions to each week's podcast episodes. → Follow Julie Holly on LinkedIn for more recalibration insights → Download the Misalignment Audit → Subscribe to the weekly newsletter → Books to read (Tidy categories on Amazon- I've read/listened to each recommended title.) → One link to all things...

What to listen for:Our hosts, Robin Greubel and Stacy Barnett, break down why "opting out" has become a buzzword that may obscure more than it reveals. While the term sounds empowering (giving dogs agency and choice), they argue it can become a self-congratulatory label that prevents handlers from addressing underlying training gaps.Stacy shares the story of 15-year-old Ray, who "opted out" of FEMA disaster work but later excelled at narcotics detection on a short lead. Ray didn't dislike detection work. Rather, she disliked working independently, far from her handler. Had Stacy recognized this earlier, she could have placed Ray in close-proximity disciplines like historic human remains detection instead of washing her out entirely.Robin recounts how one of her own dogs initially refused to search even three boxes in his front yard due to environmental overwhelm. But rather than accepting "he's opting out," she methodically built confidence through smaller areas, easier hides, and massive reinforcement. She eventually produced an elite champion! The key was asking why and adjusting the training plan, not accepting a vague opt-out label.They warn against the variable-reinforcement trap, in which dogs train handlers by occasionally succeeding, keeping handlers stuck in ineffective patterns. Stacy describes Dash's trained "collar-itch" behavior: a displacement signal she accidentally reinforced by making hides easier each time he scratched.Robin and Stacy do believe that legitimate opt-outs exist. Pain, slick floors, and overwhelming environments are just some of them. But these require specific diagnosis, not broad constructs.They advocate observable behavior analysis over anthropomorphic interpretations. This means that handlers need to teach opt-in through thoughtful progression rather than celebrating opt-out as a virtue.Key Topics:Defining Opt-Out vs. Observable Behavior (00:49)Ray's Independence Issue in FEMA vs. Narcotics Work (04:18)Environmental Confidence Building to Elite Level (07:35)Dash's Trained Collar-Itch Displacement Behavior (11:30)Variable Reinforcement and "Maybe Dogs" (15:29)Constructs vs. Specific Behavior Questions (18:40)Legitimate Opt-Outs: Pain, Slick Floors, Environmental Pressure (27:44)Teaching Opt-In from Day One with Puppies (34:31)Clever Hans Effect and Handler Cues (38:54) Resources:Dogs distinguish human intentional and unintentional action (study) We want to hear from you:Check out the K9 Detection Collaborative FB page and comment on the episode post!K9Sensus Detection Dog Trainer AcademyK9Sensus Foundation can be found on Facebook and Instagram. We have a Trainer's Group on Facebook!Scentsabilities Nosework is also on Facebook. Here is a Facebook group you should join!You can follow us for notifications of upcoming episodes, find us at k9detectioncollaborative.com to enjoy the freebies, and tell your friends so you can keep the conversations going.And don't forget to check out the YouTube Channel!

When responsibility begins to feel heavy and pressure never fully lifts, you may not be overwhelmed — you may be losing yourself inside what you carry. This isn't laziness or weakness. It's identity drift. And it can be recalibrated.When responsibility becomes your identity, even strength can start to feel suffocating.In this milestone Episode 300, we explore what happens when commitment slowly turns into consumption — when being dependable, capable, and steady becomes fused with who you are rather than something you do.Many high performers and high-capacity humans do not struggle with effort. They struggle with self-erasure.They say yes quickly.They step in instinctively.They stabilize before anyone asks.And over time, responsibility stops being a role and starts becoming proof of worth.This episode gently explores:• Why over-functioning can feel like maturity• How identity drift hides beneath competence• Why delegating can feel destabilizing, not logistical• The loneliness of being the stabilizer in every room• The subtle fear: “If I'm not the steady one, who am I?”• Why high-capacity humans are allergic to self-deception — and how recalibration is refinement, not avoidanceWe name the deeper tension beneath burnout and stress:Not exhaustion alone, but identity fusion.This is not about doing less.It is about holding responsibility without disappearing inside it.Through Identity-Level Recalibration (ILR), we are not layering on productivity tactics or mindset hacks. We begin at the root — the who. Because identity precedes behavior. When alignment becomes your default, it becomes difficult to live misaligned for long. Not because you are perfect, but because you notice sooner. You adjust sooner. You release shame faster.Pressure creates short-term results.Alignment creates sustainable strength.Three hundred conversations later, the evidence is clear:Alignment scales. Pressure doesn't.This episode offers orientation before resolution.Recognition before force.Companionship instead of correction.Today's Micro Recalibration:Before you say yes, pause.Ask yourself:Is this alignment — or identity maintenance?You don't need to change your answer immediately.Just notice.Reinforcement begins with awareness.Explore Identity-Level Recalibration → Schedule a conversation with Julie to see if The Recalibration is a fit for you → Learn about The Recalibration Cohort→ Join the next Friday Recalibration Live experience → Take your listening deeper! Subscribe to The Weekly Recalibration Companion to receive reflections and extensions to each week's podcast episodes. → Follow Julie Holly on LinkedIn for more recalibration insights → Download the Misalignment Audit → Subscribe to the weekly newsletter → Books to read (Tidy categories on Amazon- I've read/listened to each recommended title.) → One link to all things...

Struggling with impulsive behaviors and meltdowns? Discover the 5 secret micro habits that build self control in kids and how small daily shifts strengthen executive functioning and emotional regulation. With expertise in Regulation First Parenting™, Dr. Roseann Capanna-Hodge helps families decode dysregulation and build lasting calm. Self control isn't about stronger discipline or more motivation. It's a developmental brain skill built through regulated moments—not punishment. When the nervous system and executive functioning system work together, kids develop the ability to pause, delay gratification, and respond instead of react.It's not bad parenting—it's a dysregulated brain. In this episode, we unpack the 5 secret micro habits that build self control in kids and how small, daily shifts help children develop real self control—without power struggles.Why does my child lack self control even with consequences?If discipline alone worked, your child would already have self discipline.When parents describe a lack of self control, they're seeing:Impulsive behaviorsExplosive emotionsTrouble waiting or delaying gratificationAvoiding tasks that require focusSelf control depends on a regulated nervous system and strong executive functioning (including working memory, self talk, and emotional control). If either system is offline, your child simply cannot access the skill—yet.Pressure doesn't build capacity. It exposes the gap.

Nervous system leadership becomes essential when pressure and stress quietly shape team culture. If you feel responsible for the emotional tone of every room, this isn't a leadership flaw. It may be identity-level misalignment, not lack of strength.Most leaders try to fix culture with strategy.But culture is shaped long before strategy is spoken.In this episode, we explore nervous system leadership — not as theory, but as lived practice. If you've ever felt exhausted from carrying the emotional climate of your team, or confused about why tension returns even when results are strong, this conversation will meet you.This episode reinforces a simple truth:You cannot control every nervous system in the room.But you absolutely influence the tone that enters it.This is not about becoming softer.It is about becoming steadier.And steadiness is not passive. It is regulated intensity. Controlled momentum. Grounded authority.In Season 4, we are walking through the Identity-Level Recalibration pathway — moving from recognition, to release, to reclamation, and now to reinforcement. Reinforcement is where awareness becomes pattern. Where hope becomes embodied leadership.In this conversation, we explore:• Why burnout in leadership often stems from over-transmitting urgency• How pressure culture forms through shared stress responses• The difference between implied urgency and stated standards• Why many high-capacity humans became the “thermostat” long before they became leaders• How one embodied pause before entering a room can begin reshaping cultureIdentity-Level Recalibration is not another productivity tactic.It is not performance optimization.It is not a communication hack.If you've ever wondered:Why does my team mirror my stress?Why does culture feel tense even when goals are clear?Why am I tired of being the strongest nervous system in every room?You're not broken.You may simply be reinforcing patterns you learned long before you were leading.Reinforcement is hopeful because culture is responsive. Not instant. But responsive. Consistency builds trust. Steadiness compounds.Today's Micro Recalibration:Before your next interaction, pause and ask, “Am I about to transmit urgency — or steadiness?” Take one full breath. Name expectations clearly. Replace implied pressure with calm clarity.Explore Identity-Level Recalibration → Schedule a conversation with Julie to see if The Recalibration is a fit for you → Learn about The Recalibration Cohort→ Join the next Friday Recalibration Live experience → Take your listening deeper! Subscribe to The Weekly Recalibration Companion to receive reflections and extensions to each week's podcast episodes. → Follow Julie Holly on LinkedIn for more recalibration insights → Download the Misalignment Audit → Subscribe to the weekly newsletter → Books to read (Tidy categories on Amazon- I've read/listened to each recommended title.) → One link to all things...

The integration of Artificial Intelligence (AI) into post-injury rehabilitation is transforming recovery paradigms by enabling personalized, adaptive, and efficient rehabilitation pathways tailored to individual patient needs. This podcast reviews the current advances in AI applications that facilitate assessment, monitoring, and optimization of rehabilitation programs following injuries. Through machine learning algorithms, wearable sensors, and predictive analytics, AI enhances the precision of therapy plans, tracks patient progress in real-time, and predicts recovery trajectories. The discussion includes the benefits of AI-driven rehabilitation, including improved functional outcomes, reduced recovery times, and increased patient engagement. It also addresses challenges such as data privacy, algorithmic bias, and integration with clinical workflows.  1. Transforming recovery paradigms Traditional post‑injury rehab relies on periodic in‑person assessments, therapist intuition, and standardized protocols that only partially account for individual variability. AI is shifting this model toward: Continuous, data‑driven care: Instead of snapshots in clinic, rehab can be informed by near real‑time streams of kinematic, physiological, and behavioral data from wearables, smart devices, and robot interfaces. Dynamic adaptation: Therapy intensity, task difficulty, and exercise selection can be automatically adjusted based on ongoing performance, fatigue, and recovery trends, rather than fixed schedules. Precision rehabilitation: Algorithms can identify which patients are likely to respond to specific interventions (e.g., constraint‑induced movement therapy vs robotics) and tailor plans accordingly. This moves rehabilitation from a "one‑size‑fits‑many" paradigm toward precision, context‑aware therapy, analogous to precision oncology but focused on function and participation. 2. Assessment, monitoring, and optimization AI for assessment Sensor‑based movement analysis: Machine learning models process accelerometer, IMU, EMG, and pressure data to quantify gait symmetry, joint kinematics, balance, and fine motor control with higher resolution than visual observation alone. Automated scoring: AI can approximate or support standardized scales (e.g., Fugl‑Meyer, Berg Balance Scale) by mapping sensor features or video-derived pose estimates to clinical scores, reducing inter‑rater variability and saving clinician time. Continuous monitoring Home and community tracking: Wearable and ambient sensors enable monitoring of daily steps, walking speed, arm use, posture, and adherence to exercises outside the clinic, feeding rich longitudinal datasets into AI models. Real‑time alerts: Algorithms can detect abnormal patterns—such as increased fall risk, reduced limb use, or signs of over‑exertion—and flag the clinician or adjust digital therapy content automatically. Optimization and decision support Predictive models: Using historical data, AI can forecast functional gains, plateau points, or risk of complications (e.g., falls, readmission), supporting individualized goal‑setting and resource allocation. Reinforcement learning and "digital twins": Emerging work in neurorehabilitation treats rehab as a sequential decision problem, using model‑based reinforcement learning and patient "digital twins" to recommend optimal timing, dosing, and progression of interventions over weeks to months.​ 3. Technologies: ML, wearables, analytics Machine learning algorithms: Supervised ML classifies movement quality (normal vs compensatory), detects exercise type from sensor streams, and estimates clinical scores. Unsupervised learning clusters patients into phenotypes (e.g., gait patterns after stroke), revealing subgroups that respond differently to certain therapies. Reinforcement learning and contextual bandits explore which therapy adjustments yield the best long‑term functional outcomes for a given individual.​ Wearable sensors and robotics: Inertial sensors, EMG, pressure insoles, and exoskeleton sensors capture high‑frequency movement and muscle activity data during training. Robotic devices (upper‑limb exoskeletons, gait trainers) coupled with AI can modulate assistance, resistance, or task difficulty in real time based on performance and predicted fatigue. Predictive and prescriptive analytics: Predictive analytics estimate trajectories (e.g., time to independent walking, expected upper‑limb function) to inform shared decisions with patients and families. Prescriptive analytics recommend therapy intensity, modality mix, and scheduling to maximize functional gains under resource constraints. 4. Benefits: outcomes, efficiency, engagement Improved functional outcomes: Studies report better motor recovery, gait quality, and ADL performance when AI‑assisted training is used—especially when robotics and intelligent feedback are involved. Reduced recovery time and resource use: More precise dosing and earlier identification of non‑responders can reduce ineffective sessions, shorten time to key milestones, and support safe earlier discharge with robust remote follow‑up. Increased adherence and engagement: AI‑driven digital rehab platforms use gamification, adaptive difficulty, and personalized feedback to keep patients engaged in home programs, improving adherence compared to static paper instructions. Support for clinicians: Instead of replacing therapists, AI can offload repetitive measurement tasks, highlight concerning trends, and offer data‑driven suggestions, allowing clinicians to focus on relational, motivational, and complex decision‑making aspects of care. 5. Challenges and ethical considerations Data privacy and security: Rehab AI often relies on continuous collection of sensitive motion, physiological, and sometimes audio/video data, raising questions about consent, storage, secondary use, and breach risk. Approaches like federated learning and on‑device processing are being explored to reduce centralization of identifiable data while still enabling model training. Algorithmic bias and fairness: If training data under‑represent older adults, women, certain racial/ethnic groups, or people with severe disability, AI models may misestimate performance or risk for those groups, potentially widening disparities in rehab access and outcomes. Ongoing auditing, diverse datasets, and participatory design with patients and clinicians are needed to ensure equitable performance. Integration with clinical workflows: Many AI tools are developed in research settings and are not yet seamlessly integrated into EHRs, scheduling systems, or therapist documentation workflows. Poorly integrated tools risk adding documentation burden or "alert fatigue," reducing adoption. Successful implementations co‑design interfaces with frontline therapists and physicians. Regulation, liability, and trust: It remains unclear in many jurisdictions how to regulate adaptive rehab algorithms (as medical devices, clinical decision support, or wellness tools) and who is liable when AI‑informed plans cause harm.​ Transparent, explainable models and clear communication to patients about the role of AI are critical for maintaining trust. 6. Case studies and emerging trends Remote and hybrid digital rehabilitation: AI‑driven platforms providing home‑based stroke, orthopedic, or Parkinson's rehab with clinician dashboards are improving adherence and extending care beyond brick‑and‑mortar clinics. Collaborative AI for precision neurorehabilitation: Frameworks combining patient‑clinician goal setting, digital twins, and reinforcement learning exemplify "collaborative AI" that augments rather than replaces therapists.​ Multimodal personalization: Integration of movement data, EMG, heart rate, sleep, and self‑reported pain/fatigue is enabling more nuanced adaptation to daily fluctuations in capacity. Conversational AI for education and coaching: Early work is assessing tools like ChatGPT as low‑risk supports for exercise education and motivation, though they are not yet precise enough to replace professional plan design AI is moving rehab toward patient‑centered, continuously adapting, and data‑rich care, but realizing this promise depends on addressing privacy, bias, workflow, and regulatory challenges in partnership with clinicians and patients.

Relationships often strain under pressure when one person carries the emotional clarity. In this episode, we explore what changes when you stop explaining yourself — not as withdrawal, but as identity-level alignment returning to the relationship.There comes a moment in many relationships when explaining yourself no longer feels supportive — it feels exhausting.Not because you don't care. Not because you're shutting down. But because clarity no longer needs performance to feel safe.In this episode of The Recalibration, we explore what actually changes in a relationship when you stop over-explaining, over-functioning, or smoothing the emotional moment. Especially for high-capacity humans and deeply responsible people, explanation often became the bridge — the way connection stayed intact, misunderstandings were prevented, and closeness felt secure.But over time, that bridge can quietly become a burden.This episode sits in the Reinforcement stage of Identity-Level Recalibration, where alignment isn't built through insight alone — it's built through repetition. Not rushing to manage the moment. Not rescuing the space. Practicing steady presence without self-erasure.We explore:Why over-explaining was never about communication, but about safetyWhat “clean discomfort” feels like when you stop managing connectionHow nervous system regulation shows up as steadiness rather than silenceWhy consistency — not intensity — is what rebuilds relational trustThis is not about becoming distant or withholding. It's about allowing your presence to speak without justification.Unlike mindset work or communication strategies, Identity-Level Recalibration (ILR) doesn't ask you to perform differently — it helps you be differently. When identity realigns, behavior follows naturally. That's why this work feels quieter, slower, and more embodied — especially inside intimacy.This episode is part of a week-long relational arc exploring how recalibration unfolds in real relationships — and why stopping explanation isn't abandonment, but alignment practicing itself.Today's Micro RecalibrationNotice where you feel the urge to explain yourself — even when you already know what's true. Don't stop it. Don't act on it. Just stay present and see what steadiness communicates on its own.Explore Identity-Level Recalibration→ Join the next Friday Recalibration Live experience → Take your listening deeper! Subscribe to The Weekly Recalibration Companion to receive reflections and extensions to each week's podcast episodes. → Follow Julie Holly on LinkedIn for more recalibration insights → Schedule a conversation with Julie to see if The Recalibration is a fit for you → Download the Misalignment Audit → Subscribe to the weekly newsletter → Books to read (Tidy categories on Amazon- I've read/listened to each recommended title.) → One link to all things